ALL RIGHT.
SO WELCOME EVERYBODY.
WE HAVE 56 PEOPLE AND TODAY IS A
RECAP OF DAY ONE AND I MAY HAVE
SOME MISSPELLS BUT I'M GOING TO
TRY IN 20 MINUTES TO RECAP EIGHT
HOURS OR SO OF WORK.
THIS IS A RAPID FIRE VIM.
-- SUMMARY.
I SUGGESTED WE'LL LOOK AT DATA
AND GET TO INSIGHTS AND THE
INSIGHTS RELATE TO AVAILABLE
DATA INDIVIDUALLY INITIATED DATA
WITH A DIGITIZATION OF A LOT OF
INFORMATION AND RESEARCH
AGGREGATION AND ANALYSIS TO
IMPROVE OPTIMUM HEALTH.
COMPUTING POWER HAS INCREASED A
MILLION FOLD AND THAT'S AN
AMAZING REVOLUTION AND IT'S
GOTTEN CHEAPER FIRST TALK WAS
FROM ERIC TOPOL AND THERE WAS
READING COMPREHENSION GETTING
BETTER AND BETTER FOR MACHINES
AND THEN OTHER TASKS ARE
CATCHING UP.
I THINK HE EMPHASIZED MULTIMODAL
SELF-SUPERVISED TRAINING AND
AOMICS AND GRAPHS COMBINED WITH
MEDICAL DOMAIN KNOWLEDGE TO BE
THE GET FLEXIBLE INTERACTIONS
WITH PHYSICIANS AND PATIENTS.
I THINK HE SHOWED US A NUMBER OF
ANECDOTES AND SHOWED US SOME
DATA.
THIS IS ONE OF HIS DATA SLIDES
WHERE HE SHOWED SPECIALIZED
FINE-TUNED MEDICAL LARGE
LANGUAGE MODELS HAVE NOW GOTTEN
QUITE GOOD AND WHEN THEY USE
JUST A NON-SPECIALIZED MACHINE,
THEY GET PREDICTION RATES AND
ACCURACIES THAT ARE NOT VERY
GOOD BUT COMBINING THINGS YOU
CAN GET MUCH BETTER SO I THINK
IT'S THE PROMISE OF THE FUTURE
TO GET BETTER ACCURACY WHEREAS
NOW THERE'S LOTS OF INTERESTING
ANECDOTES BUT WE'RE IMPROVING
AND IMPROVING AND THERE'S
IMPRESSIVE PROGRESS IN A.I. AND
RADIOLOGY.
SO THAT WAS THAT.
THEN WE SWITCHED TO MANISH DESAI
AND LOOKING AT ATRIAL
DEFIBRILLATION COMPARED TO A
CLINICAL DEVICE
AND THE STUDY ENROLL 400,000
PEOPLE.
NOTIFICATIONS WERE RARE WHICH
SAY GOOD THING OTHERWISE YOU
HAVE TOO MANY FALSE POSITIVES.
COMPLETION RATE WERE A CHALLENGE
AND TALKED ABOUT THE CHALLENGES
OF DATA CLEANING, LINKAGE,
KNOWING YOU HAVE ACTUALLY THE
SAME PEOPLE IN REAL WORLD
STUDIES SO THIS WAS A GOOD
DEMONSTRATION OF HOW MUCH HARDER
A REAL WORLD STUDY CAN BE AND
WITH PILOT STUDIES THEY GOT TO
SUCCESS.
NEXT WE TALKED ABOUT BIG DATA
FOR PUBLIC HEALTH RESEARCH BY
RANDI FORAKER AND SHOWED THE N3C
DATA INTEGRATION WHICH WAS
IMPRESSIVE IN GETTING TO BOTH
HARMONIZATION OF LARGE DATA SETS
AND THEN SPENT A FAIR AMOUNT OF
TIME ON THE CONCEPT OF SYNTHETIC
CLINICAL DATA.
THAT WAS EXCITING IN THAT YOU
BALANCE THE IDEA THAT IF YOU
WANT TO MAXIMALLY PROTECT
PRIVACY YOU BECOME
UNINFORMATIVE.
IF YOU WANT TO MAXIMALLY PROTECT
UTILITY THEN IT CAN'T BE
SYNTHETIC AND HAS TO BE
IDENTIFIABLE AND PROTECTED BUT
YOU CAN GET STRE REASONABLE
COMPROMISES OF GRADING SYNTHETIC
DATA FOR SPECIFIC PURPOSES WHICH
IS DIFFERENT THAN DE-IDENTIFIED
DATA AND THEY CREATED MULTIPLE
DATA SETS AND YOU CAN REQUEST OF
SOME BY TALKING TO HER.
THE LESSONS LEARNED IS THAT
SYNTHETIC DATA CAN ENHANCE A.I.
MACHINE LEARNING, ACCELERATE
RESEARCH, FACILITATE SHARING AND
EXPAND DATA ACCESS.
THEN WE WENT TO INTEROPERABILITY
OF MACHINE IMAGING AND BEYOND BY
DAVID MENDELSON AND EMPHASIZED
HAVING COMMON RULES AND
EVERYBODY OBEY THE SAME RULES
WITH WELL DEFINED TECHNICAL
SPECIFICATIONS AND HOW IT WAS
FROM THE STANDARDS AND HAVING A
PARTICIPANT DIRECTORY SO WHICH
USER IS USING FOR WHAT PURPOSE.
AND THEN HE SHOWED THAT
PATIENT-CENTERED CLINICAL AND
IMAGING DATA SHARING IS COMING
AND MORE IN THE FUTURE BUT THE
IDEA IS TO HAVE E.H.R. PATIENT
PORTALS, SHARING SCREENS AND
THEN THE PATIENT CAN CONTROL WHO
GETS TO SEE WHICH DATA AND FOR
EXAMPLE FIRE AND IMAGES AND
PROTOCOLS CAN ALLOW FOR CLINICAL
CONSUMER AND RESEARCH.
SO IT'S QUITE EXCITING TO BE
ABLE TO GET AGGREGATION ACROSS
MANY HOSPITAL HEALTH SYSTEMS AND
ONE PERSON TURNED THEIR CAMERA
ON, THANK YOU VERY MUCH.
I LIKE SEEING PEOPLE, THIS IS
GOOD.
WE'RE IN OUR RAPID FIRING.
THE CONCLUSIONS WERE
INTEROPERABILITY ARE ACHIEVED
WEN STANDARDS ARE FOLLOWED AND
THEY'RE NOT EXCITING BUT THEIR
CRITICAL AND REAL WORLD
SOLUTIONS PROVIDING THE BASIS
FOR RESEARCH AND COLLABORATION
WITH THE IDEA THAT WE CAN'T HAVE
HEALTH SYSTEMS OWNING PEOPLE'S
DATA.
WE NEED TO HAVE IT SHARED
ACCORDING TO THE STANDARDS
BROADLY.
FROM THERE WE WENT TO MICHAEL
SNYDER.
VERY DYNAMIC AND POWERFUL
INSPIRATIONAL TALK.
AND THE IDEA WAS TO LONGITUDINAL
PERSONAL OMIC PROFILE.
MORE DATA PER PERSON THAN YOU
CAN EVER IMAGINE.
OFTEN EPIDEMIOLOGY WAS 10,000
PEOPLE AND 1,000 VARIABLES.
HERE WE'RE TALKING ABOUT 10
PEOPLE AND 10 MILLION VARIABLES
AND CHALLENGE YOU HOW MAKE SENSE
OF THAT.
I THINK HE SHOWED US TOOLS ARE
NEEDED TO MAKE SENSE OF THAT AND
SHOWED A FUN AND INTRIGUING
EARLY COVID DETECTION SYSTEM AND
YOU USE A FIT BIT AND LOOK AT
THE HEART RATE GOING UP AND IT
TURNS OUT IT GOES UP FOUR DAYS
BEFORE COVID SYMPTOMS.
THAT COULD BE POWERFUL IF WE
COULD DO IT WELL AND SORT OF GET
AHEAD OF EPIDEMICS TO ISOLATE
BUT YOU HAVE TO HAVE PEOPLE
TRUST THE DATA AND HE DECIDED TO
FLY DISHIS -- DESPITE HIS HEART
RATE SIGNAL AND OTHER STRESS
TRIGGERS TRIGGER THE ALG MYTH
AND STRESS, INTENSE EXERCISE,
TRAVEL, OTHER THINGS TRIGGER THE
ALGORITHM AND I ADDED THIS, I
THINK WE NEED TO THINK EVEN ON
AN INDIVIDUAL PERSONAL LEVEL HOW
DO WE ASSESS ACCURACY THINGS
LIKE POSITIVE PREDICTIVE,
NEGATIVE PREDICTIVE VALUE,
SENSITIVITY, SPECIFICITY AND
REMEMBER IT'S ALL A FUNCTION OF
FRAMEWORK AND LIKELY TIMES THE
PRE TEST ODDS AND I THINK WE
NEED TO INTEGRATE BAYESIAN
THINKING INTO WELL, IN THIS
ALGORITHM SAYS IT'S RED AND I'LL
HAVE COVID IN FOUR DAYS HOW
LIKELY IS THAT AND THAT WILL BE
AF
AFFECTED BY WHO I AM AND WHO WAS
I EXPOSED AND DECIDING TO FLY OR
TAKE A MEDICATION OR HOSPITALIZE
MY SELF.
THIS IS EXCITING BUT LOTS TO DO.
IT WAS INTERESTING TO SEE HE
CO-FUNDED 17 COMPANIES AND
THERE'S TONS OF EXCITEMENT IN
THIS SPACE.
VENTURE CAPITAL PLAYS BY
DIFFERENT RULES THAN THE NIH.
THIS IS AN NIH SPONSORED
WORKSHOP AND IT'S ABOUT
INTEGRATING AND HOW DO WE
BALANCE ALL THOSE THINGS.
THE GOOD THING IS WE'RE IN SLIDE
22 OUT OF 42 AND MAY MAKE IT.
WE'RE AT 161 ATTENDEES.
THAT'S WONDERFUL AND TECHNICALLY
WE'RE DOING WELL.
SO THEN WE HAD RASHMEE SHAH AND
HAD A SUGGESTION TO BUILD
MULTIPURPOSE SYSTEM AND EACH
LABEL CAN OUT SURVIVE THE
PROJECT AND THEN THAT PROJECT
WOULD THEN TRANSFER KNOWLEDGE TO
THE NEXT PERSON SO IT'S THE
FIRST PROJECT WELL DEFINED
ATRIAL DEFIBRILLATION AND THE
NEXT PROJECT CAN HAVE THAT
UNDERSTANDING AND BENEFIT FROM
IT.
I THINK WE ARE EVOLVING TO THOSE
SYSTEMS WITH REGISTRIES AND
OTHER THINGS AND THINK THE
INTEGRATION AND SHARING IS
IMPORTANT.
NEXT WE HAD A TALK BY EBONY
BOULWARE FROM WAKE FOREST FROM
EQUITY FRAMEWORK IN THE USE OF
BIG DATA.
REALLY IMPORTANT NOT TO FORGET
WE HAVE HUGE DISPARITIES
GLOBALLY AND IN THE U.S.
HIGH BLOOD PRESSURE STARTS OUT
NOT BEING THAT DIFFERENT AT
YOUNG AGES AND THEN AS WE GET
OLDER, AFRICAN AMERICANS HAVE A
DISPROPORTIONATE BURDEN OF BLOOD
PRESSURE, DIABETES AND STROKE.
OUR SYSTEM HAS A PROBLEM AND WE
NEED TO ADDRESS THIS ISSUE OF
DISPARITIES BECAUSE OVER ALL OUR
PERFORMANCE IS JUDGED BY OUR
OVER ALL PERFORMANCE AND IT'S
THE RIGHT THING TO DO.
SHE SUGGESTED DISPARITIES ARE A
LAYERED COMPLEX DETERMINATES
SYSTEM.
IT'S NOT JUST CELLS, IT STARTS
WITH COMMUNITIES.
WHEN DISCRIMINATION STARTED
HISTORICALLY WITH REDLINING IT
CONTINUES.
THERE'S FUNDAMENTAL SOCIAL NORMS
AND POLICIES, INSTITUTIONS,
CONTEXT, INDIVIDUALS AND
EVENTUALLY GETS EXPRESSED IN
BIOLOGY AND IT'S FASCINATING TO
STUDY AT EVERY SYSTEM AND CAN
TRY TO FIX IT PROBABLY AT THE
ROOT CAUSE OF SOCIAL AND POLICY
BUT IF IT GETS TOO LATE YOU HAVE
SOMEBODY WITH HYPERTENSION,
DIABETES OR STROKE YOU CAN USE
THE HEALTH SYSTEM TO ADDRESS THE
INEQUALITIES AT EACH STEP.
SHE GAVE US SOME EXAMPLES AND
MACHINE LEARNING GETTING THINGS
WRONG IF IT'S ONLY TRAINED IN
LIGHT-SKINNED PATIENTS AND
THERE'S EVIDENCE THE COLOR OF
YOUR FINGER MATTERS AS TO WHAT
THE PULSE OX READS AND THE
MACHINES HAVE NOT BEEN TRAINED
FOR PEOPLE WITH EVERY SKIN COLOR
SO WE NEED TO BE CAREFUL.
SHE SUGGESTED THAT DATA BIGNESS
DOES NOT CHANGE THE FUNDAMENTAL
EQUITY PRINCIPLES AND WE NEED TO
BE AWARE THAT DATA AND ANALYTIC
TECHNIQUES CAN MANIFEST IN BIAS
DUE TO ASSUMPTIONS, DIFFERENCES
IN DATA, DIFFERENCES EN
COMPLETE -- IN COMPLETENESS
BEING LINKED AND DATA HOURSES
NOT HARMONIZED MAY BE DIFFERENT
BY THINGS RELATED TO
INEQUALITIES AND SOCIAL STATUS.
AND IN SUMMARY SHE SUGGESTED
THERE'S A LOT OF PROMISE SO
SHE'S AN OPTIMIST BUT THERE ARE
REAL PITFALLS AND WE NEED AN
EQUITY MIND SET IS WHAT SHE
SUGGESTED.
CRITICAL TO ENSURING BIG DATA
STUDIES ARE JUST VALID AND
GENERALIZABLE.
I THINK WE NEED TO BE TECHNICAL.
WE NEED TO BE SMART BUT NEED TO
REMEMBER THESE THINGS AND
INCLUDE THAT IN THE IS SUMMARY
FROM THE WORKSHOP.
THEN WE GOT TECHNICAL.
KEENAN WALKER SHOWED US HIS
UNDERSTANDING OF ALZHEIMER'S
DISEASE BIOLOGY AND I THINK TO
ME THE IMPRESSIVE THING IS THAT
HE DREW 10 DIFFERENT LINES OF
EVIDENCE FROM DIFFERENT COHORTS.
SO THIS IS AN EXAMPLE WHERE HE
SHOWED SYNAPTIC FUNCTION AND
EXTRA CELLULAR MATRIX
ORGANIZATION AND HAD SIGNALS
FIVE TO 10 YEARS AND 25 YEARS
BEFORE DEMENTIA.
AND THEN HE WAS ABLE TO SHOW
THAT CERTAIN MOLECULES WERE
LIKELY TO BE CAUSAL AND SOME
WERE NOMINATED AS THERAPEUTIC
TARGETS AND HE WAS ABLE TO
INTEGRATE EVIDENCE FROM ONE
COHORT, ANOTHER COHORT, BLOOD,
CSF, ETCETERA.
SO THAT I THINK IS THE FUTURE OF
INTEGRATION.
THE ULTIMATE IN INTEGRATION AND
COLLABORATION IS THE U.K. BIO
BANK IS SOMETHING WE CAN LEARN A
LOT FROM AND THAT WAS IMPRESSIVE
FROM MEGAN CONROY TELLING US AT
THIS POINT, NOT THE FUTURE,
CURRENTLY, U.K. BIO BANK HAS
ENORMOUS DATA AND BIO CHEMISTRY
ON 500,000 PEOPLE AND
METABOLOMICS AND DNA AND IMAGING
ON 100,000 PEOPLE AND
IMPRESSIVELY ANALYSIS PLATFORM
THAT ALLOWS EVERYBODY TO ANALYZE
IT IN ONE PLACE WITH 5,000 USERS
AND HOW WE CONNECT THIS TO OTHER
STUDIES AND DO SIMILAR THINGS
BUT THERE'S A LOT TO BE LEARNED.
IT WAS NEAT THAT AFTER NILANJAN
CHATERJEE TALKED ABOUT COMBINING
DATA ACROSS PARTIAL DATA SETS.
WE CANNOT HAVE THE MICHAEL
SNYDER 12 OMICS PER PERSON ON
EVERY PERSON IN EVERY STUDY IN
EVERY HEALTH SYSTEM.
THIS IDEA OF HOW TO TRANSFER
INFORMATION AND INTEGRATE
INSIGHTS, IT WAS TECHNICAL BUT I
THINK IT ADDRESSES THE COMMON
IMPORTANT GAP IN OUR KNOWLEDGE.
GENERALLY WE HAD AN EXCITING DAY
AND HAD A TUTORIAL THIS MORNING
AS WELL.
I WILL TURN IT TO JESSILYN AND
FIX THE SPELLING OF HER NAME
WHILE MAYBE SHE MAKES A COMMENT
ABOUT THIS.
WITH APOLOGIES, WELCOME AND GOOD
MORNING, PROFESSOR DUNN.
>> THANK YOU, DR. CORESH.
IT WAS A FANTASTIC RECAP OF
YESTERDAY.
THERE WAS A LOT OF MATERIAL TO
GO THROUGH AND I THINK YOU
DISTILLED IT TO A POINT WHERE
FOR FOLKS WHO MIGHT HAVE MISSED
ONE OR TWO TALKS OR MAYBE EVEN
THE ENTIRE DAY HOPE FULLY
THEY'RE RELATIVELY CAUGHT UP AND
CAN CATCH THE SLIDES OF THE
TALKS THEY MIGHT BE MORE
INTERESTED IN CATCHING UP ON AND
SO WITH THAT I AM THRILLED TO
INTRODUCE OUR KEY NOTE SPEAKER.
WE HAVE AN EXCITING
>> EXCUSE ME, LET THE TECHNICAL
PEOPLE CUE THIS UP AND WE MAY
HAVE TWO MINUTES FOR QUESTIONS.
WE'RE AHEAD BY THREE MINUTES AS
MY CLOCK SHOWED.
>> YOU ARE CORRECT.
WE SURE ARE.
THAT WOULD BE FANTASTIC IF WE
CAN GET OUR SLIDES UP.
IF THERE'S ANY QUESTIONS FEEL
FREE TO PUT THEM IN THE Q&A BOX.
WE ASK THAT YOU PUT THEM IN THE
Q&A BOX RATHER THAN THE CHAT AND
HELP US KEEP TRACK OF THEM.
>> WE'RE GETTING PEOPLE UP AND
GOING AND YOU CAN GIVE US CHATS
IF YOU REFER BUT WE'LL BE -- WE
HAVE THREE MINUTES.
>> WE'LL HAVE THREE-DIFFERENT
SPEAKERS WHO ARE GOING TO BE
TALKING ABOUT HOW BIG DATA AND
DATA INTEGRATION TECHNIQUES ARE
APPLIED IN REAL WORLD
CARDIOVASCULAR EPIDEMIOLOGIC
RESEARCH AND WITH THE SESSIONS
WE HAD TUTORIALS THAT HAVE
HANDS-ON EXAMPLES.
SO FOR ANYBODY WHO MAY HAVE
MISSED THE TUTORIALS WE HAVE
RECORDINGS AVAILABLE TO GET A
HANDS ON EXAMPLE HOW THIS
RESEARCH WAS PERFORMED.
IN THE AFTERNOON WE'LL HAVE
ANOTHER KEY NOTE ON NAVIGATING
GENOMICS DATA AND CURRENT TOOLS
AND TECHNIQUES AND HAVE AN
EXCITING AFTERNOON SESSION OF
FIVE DIFFERENT SPEAKERS TALKING
ABOUT FUTURE TRENDS, NOVEATIONS
AND APPLICATIONS OF BIG DATA AND
EPIDEMIOLOGY.
SO WITH THAT I THINK WE HAVE A
PRETTY EXCITING DAY PLANNED.
I DON'T KNOW THAT THERE ARE ABE
QUESTIONS THAT HAVE POPPED UP
INTO THE Q&A
>> THERE'S ONE QUESTION.
DO WE HAVE IN THE U.S. ANYTHING
LIKE THE U.K. BIO BANK.
>> WE HAVE THE PERFECT ANSWER
RIGHT NOW.
>> WE'LL HEAR ABOUT THE U.S. s
VERSION OF THIS SORT OF LARGE
SCALE POPULATION LEVEL STUDY THE
"ALL OF US" INITIATIVE WE'LL
HEAR FROM THE "ALL OF US" CEO
DR. DENNY ABOUT.
SO BEFORE WE GET THAT SESSION
KICKED OFF, DR. CORESH IS THERE
ANYTHING ELSE YOU WOULD WANT TO
MENTION?
>> I THINK WE ARE PERFECTLY ON
TIME.
YOUR INTRODUCTION WAS LOVELY AND
LET'S GO.
>>
>> SOUNDS GREAT.
I AM PLEASED TO INTRODUCE
DR. DENNY WHO IS THE CHIEF
EXECUTIVE OFFICER OF THE
NATIONAL INSTITUTES OF HEALTH,
"ALL OF US" RESEARCH PROGRAM.
"ALL OF US" SEEKS TO ENROLL AT
LEAST 1 MILLION DIVERSE
PARTICIPANTS TO BUILD AN
INDISPENSABLE RESOURCE TO
ACCELERATE PRECISION MEDICINE
FOR ALL POPULATIONS.
PRIOR TO JOINING NIH JOSH WAS A
PROFESSOR OF INFORMATICS AND
MEDICINE AND FOUNDING DIRECTOR
OF THE CENTER FOR PRE VISION
MEDICINE AND VICE PRESIDENT FOR
PERSONALIZED MEDICINE FOR
VANDERBILT MEDICAL CENTER AND
PRACTICING PHYSICIAN.
HE LED DISCOVERY AND PROJECTS IN
PRECISION MEDICINE INCLUDING
CLINICAL PHARMACO GENOMIC AND
VANDERBILT'S DNA BIO BANK.
HE WAS A PIONEER IN THE RECORDS
FOR GENOMIC STUDIES INCLUDING
THE INITIAL DESCRIPTIONS OF
PHENOME WIDE ASSOCIATION
STUDIES, PHEWAS AND PHENOTYPE
RISK SCORES.
IN ADDITION TO HIS ROLE WITH
"ALL OF US" HE OVERSEES THE
PRECISION HEALTH INFORMATICS
SECTION AT THE NHGRI AND ELECTED
MEMBER OF THE NATIONAL ACADEMY
OF MEDICINE AND AMERICAN SOCIETY
FOR CLINICAL INVESTIGATION AND
THE AMERICAN COLLEGE OF MEDICAL
INFORMATICS.
YOU CAN FOLLOW JOSH ON TWITTER
AT ALL OF US CEO.
>> THANK YOU, DR. DUNN.
IT'S GREAT TO BE HERE AND TALK
WITH ALL OF YOU AND COMPLIMENT
WHAT AN INCREDIBLE SHORT SUMMARY
OF A FULL DAY OF ACTIVITIES IT
WAS INCREDIBLE.
I AM GOING TO TALK ABOUT THE
"ALL OF US" RESEARCH PROGRAM
WHICH IS WE ARE SIMILAR AND SORT
OF GREW AND HAVE LEARNED A TON
FROM OUR CLOSE COLLEAGUES IN THE
U.K. BIO BANK AND TO THAT SET UP
AND THAT QUESTION.
SO PART OF WHAT WE'RE TRYING TO
DO CERTAINLY LEVERAGE MULTIPLE
DATA SOURCES.
AS WE'RE THINKING ABOUT THE
FRAMING OF THIS AND THE PLANNING
FOR THIS EFFORT OF THE PRECISION
MEDICINE INITIATIVE YOU MAY
RECALL, WE TALKED ABOUT THE
TRANSFORMATIONAL DIFFERENCE A
LOT OF COHORTS HAVE MADE SUCH AS
THE FRAMINGHAM HEART STUDY WITH
WHAT WE WOULD CALL A MODEST
NUMBER OF PEOPLE ENROLLED.
CERTAIN NON-DIVERSE.
THIS IS THE FIRST PAPER 13 YEARS
AFTER IT STARTED AND LOTS OF
FOUNDATIONAL KNOWLEDGE THAT
TRANSFORMED OUR UNDERSTANDING OF
HEART DISEASE, STROKE AND RISK
FACTORS PROVIDING FOUNDATIONAL
KNOWLEDGE AND THESE WERE
INTERVENABLE THINGS WE CAN MAKE
AN ON AND THESE AND OTHER
COHORTS WITH THEIR TREATMENTS
HAVE HELPED US SEE DRAMATIC 50,
70% REDUCTION IN CARDIOVASCULAR
AND RELATED DISEASES IN THE TIME
SINCE THE COHORTS WERE LAUNCHED.
HOW COULD WE DO THIS WITH THE
PRECISION MEDICINE?
WE WANT TO ACCELERATE MEDICAL
BREAKTHROUGHS AND INDIVIDUALIZED
PREVENTION TREATMENT AND CARE
FOR ALL OF US.
THE LAST PART IS KEY.
THERE'S LOTS OF EFFORTS WE FELT
WORLDWIDE ESPECIALLY IN GENOMICS
THAT FOCUSSED ON EUROPEAN
ANCESTRIES, SELF-DECLARED WHITE
INDIVIDUALS AND WE WANTED TO
REFLECT RICHLY THE DIVERSITY OF
THE UNITED STATES.
WE WANTED TO ENROLL 100
PARTICIPANTS AND BE
LONGITUDINAL.
A PROCESS THAT WOULD LAST
DECADES AS A PARTNERSHIP WITH
OUR PARTICIPANTS NOT RESEARCH
SUBJECTS.
WE WANT TO DELIVER A LARGE, RICH
BIO MEDICAL SET AND MAKE IT
AVAILABLE TO AS MANY RESEARCHERS
AS POSSIBLE AND HAS TO BE A
COMMUNITY.
WE SIT WITHIN THE O.D. AND SERVE
AS A PLATFORM FOR THE REST OF
THE NIH AND WE WANTED TO BE A
LOCAL ECO SYSTEM OF RESEARCHERS,
FUNDERS AND COMMUNITIES.
OUR PARTICIPANT COMMUNITIES ARE
IMPORTANT NOW.
WE'LL EVENTUALLY MAKE "ALL OF
US" AN INDISPENSABLE PART OF
HEALTH RESEARCH.
WE HAVE ENROLLED 700,000 PEOPLE
SINCE WE LAUNCHED IN MAY OF
2018.
WE REPRESENT ALL 50 STATES AND
MOST U.S. TERRITORIES.
WE HAVE WHERE WE HAVE MORE
INDIVIDUALS IN STATES
REPRESENTED ARE GENERALLY WHERE
WE HAVE MORE BRICK AND MORE
MOR
MORTAR AND WE HAVE SALIVA KITS
AND WE HAVE COMPANIES THAT CAN
GO TO YOUR HOUSE AND 60 OR SO
MEDICAL CENTERS AND OTHER HEALTH
CARE PROVIDER ORGANIZATIONS
PARTNERED WITH US INCLUDING THE
VETERANS AFFAIRS HOSPITALS.
ABOUT 500,000 CONTRIBUTED BIO
SPECIMENS AND THE MINIMUM IS DNA
BUT MOST CONTRIBUTE BLOOD AS
WELL WHICH GIVES US SERUM,
PLASMA, DNA AND URINE IS
COLLECTED AS WELL.
>> THERE'S A TINY BIT OF DELAY
BUT YOU CAN SAY NEXT SLIDE.
>> ALL RIGHT.
CAN SOMEONE ELSE ADVANCE?
IT'S NOT DOING ANYTHING FOR ME.
THERE WE GO.
I MENTIONED OUR DIVERSITY.
FOR NOW I'LL SAY NEXT SLIDE.
WE FOCUSSED ON DIVERSITY AND
WE'RE ABOUT HALF INDIVIDUALS
SELF-DECLARING RACE AND
ETHNICITY OTHER THAN
NON-HISPANIC WHITE AND UNDER
REPRESENTS IS NOT LIMITED TO
RACE AND ETHNICITY AND YOU CAN
SEE OTHER DIMENSIONS OF
DIVERSITY WE CAPTURED INCLUDING
AGE, INCOME, DISABILITY IS ONE
WE ADDED THROUGH THE COURSE OF
THE PROGRAM AND THEN CAUGHT UP
OUR PARTICIPANTS WITH A
SUPPLEMENTAL SURVEY AND SEXUAL
AND GENDER MINORITIES AND
LOCATION AND THINGS LIKE THAT AS
WELL.
>> WE'VE LEARNED PERIODICALLY
IT'S A STRESS TEST.
WE TAKE AWAY YOUR SLIDES AND SEE
WHAT HAPPENS.
>> GOING BACK TO THE SLIDE, OVER
ALL MORE THAN 80% OF OUR
PARTICIPANTS ARE UNDER
REPRESENTED BIO MEDICAL RESEARCH
FOR THE PROGRAM AND AS WE
ALREADY TALKED ABOUT THE KINDS
OF INFORMATION THAT WE COLLECT
AND TO SURVEYS AT BASELINE,
ELECTRONIC HEALTH RECORDS ARE A
KEY PART OF THAT AND WITH THOSE
WE ALSO ARE CAPTURING FROM A
NUMBER OF SITES FROM ACROSS THE
COUNTRY.
AND I'M GUESSING SLIDES AREN'T
WORKING.
SOMEBODY ELSE CAN ADVANCE THEM.
SHOULD I TRY TO SHARE MY SCREEN.
>> WHOEVER IS ADVANCING THE
SLIDES, PLEASE ADVANCE THE NEXT
SLIDE.
>> GREAT.
SO HERE'S THE NEXT SLIDE.
THE KIND OF INFORMATION THAT WE
ARE COLLECTING FROM INDIVIDUALS
IN ADDITION TO THE TOP TWO I
ALREADY MENTIONED.
WE HAVE A BRIEF IN PERSON EXAM
WITH PHYSICAL MEASURES AND I
MENTION THE MEASURES AND WE HAVE
DEVICE DATA AND WE HAVE GIVEN
OUT MORE THAN 30,000 DEVICES TO
INDIVIDUALS AND WE'LL BE GIVING
OUT MORE AS WELL.
THESE ARE LONGITUDINAL IN THE
PROCESS.
SO THE NEXT SLIDE TALKS ABOUT
INFORMATION RETURN AND E.H.R.
DATA.
THIS GETS TO THE MULTIPLE
SOURCES OF INFORMATION.
SO WE GET -- THE ELECTRONIC
HEALTH RECORD AUTHORIZATION FROM
PATIENTS AND HEALTH CARE
PROVIDERS ORGANIZATIONS SHARE
THAT DATA WITH US.
THE DATA GETS HARMONIZED USING
THE PLATFORM AND WE ALSO
UNDERSTAND OUR HEALTH CARE
SYSTEM IS FRAGMENTED.
WE LAUNCHED DURING COVID THE
ABILITY FOR PEOPLE TO SHARE
THEIR ELECTRONIC HEALTH RECORDS
THROUGH INTERFACE THAT CONNECTS
WITH THE END POINTS OF MORE THAN
1,000 -- ACTUALLY WE HAVE 5,000
OR 6,000 SITES CONTRIBUTED DATA
NOW THROUGH THE PARTICIPANTS
ACTUALLY LINKING THE SITES IN
AND THEY SHARE IT TO US AND WE
HAVE NOT ACTUAL LY HARMONIZED
THAT WITHIN THE DATA MODEL YET
BUT SOMETHING WE'RE WORK ON
ACTIVELY AND SOME AUTHORIZE MORE
THAN 10 DIFFERENT SITES FOR
HEALTH CARE.
ANOTHER NICE THING ABOUT THAT IS
YOU GET TO SEE YOUR INFORMATION
AFTER LINKING IT UP AND IT'S
HARMONIZED WITHIN THE INTERFACE
AND YOU CAN SEE THE LDLs ACROSS
A NUMBER OF DIFFERENT SITES,
FRENS.
FROM THE BEGINNING WE WANTED TO
RETURN VALUE TO PARTICIPANTS,
THE RETURNING RESULT IS PERHAPS
THE KEY KIND AND ONE ASPECT IS
AROUND WHAT OUR PARTICIPANTS
TOLD US WAS MOST IMPORTANT WAS
GENETIC INFORMATION.
WE ALSO RETURN OTHER THINGS AND
AGGREGATE STUDY RESULT AND THIS
GETS INTO THE KIND OF
INFORMATION WE RECOVERED.
WE STARTED WITH ANCESTRY AND
TRAITS WHICH IS AN ENGAGEMENT
TYPE OF ASPECT.
AS YOU'D EXPECT HIGH LEVEL
SUBCONTINENTAL ANCESTRY AND
RIGHT NOW MINIMAL SET UP FOR
TRAITS NON-HEALTH RELATED.
YOU CAN SEE 137,000 PEOPLE
LOOKED AT THAT NOW.
END OF LAST YEAR WE LAUNCHED OUR
HEALTH-RELATED RETURN OF GENETIC
INFORMATION WHICH WE WORKED WITH
THE FDA AND WE HAVE AN
INVESTIGATIONAL DEVICE EXEMPTION
FOR THIS INCLUDING HEREDITARY
DISEASE RISK AND PHARMACO
GENETICS AND DNA.
THE PHARMACO GENETICS PIECE IS
TIED TO GUIDELINES THAT WE CAN
CALL WELL AND WORKING WITH THE
FDA SINCE IT INVOLVES REGULATED
MEDICATIONS IN THAT PROCESS AND
WE'RE SEEING A LITTLE LESS THAN
3% OF PEOPLE HAVE ACTIONABLE
VARIANTS IN THE 59 GENES.
OVER 96% OF PEOPLE HAVE A
PHARMACO VARIANT THAT WOULD
ALTER DRUG PRESCRIBING IF
EXPOSED TO THE MEDICATION AND
THAT'S PRETTY CONSISTENT WHILE
THE VARIATIONS IN THE
MEDICATIONS THAT WOULD BE
INVOLVED ARE DIFFERENT BY
DIFFERENT GENETIC ANCESTRIES.
THE PERCENT OF PEOPLE IS PRETTY
CONSISTENT ACROSS ALL GENETIC
ANCESTRIES.
SO NOW WE'RE GETTING TO DATA
HARMONIZATION IN SUM.
AND SO WE HAVE MULTIPLE TIERS OF
DATA AS WE THINK ABOUT IT.
THE FIRST PART IS TO SAY WE HAVE
DATA FEEDS THAT COMES INTO OUR
RAW DATA REPOSITORY AND THERE'S
METHODOLOGY APPLIED AND THERE'S
IDENTIFIERS AND DATA
HARMONIZATION ACCORDING TO THE
DATA MODEL.
YOU CAN EXPLORE THE DATA NOW, NO
LOG-IN REQUIRED ACROSS THE WORLD
THROUGH THE DATA BROWSER.
AND TWO AVAILABLE TIERS FOR
INDIVIDUAL DATA ACCESS THROUGH
THE CONTROL AND REGISTERED TIER
THROUGH THE RESEARCH OR WORK
BENCH.
I'LL TALK MORE ABOUT THAT LATER
AND WE ALSO ALLOW ACCESS TO
PARTICIPANTS AND THERE'S CONTACT
EFFORTS.
THERE'S A LARGE EFFORT FOR
PRECISION HEALTH COLLECTING
OTHER KINDS OF DATA LIKE
MICROBIOME AND DETAILED DIET AND
WILL DO CONTROLLED FEEDING
STUDIES AS WELL.
THE NEXT SLIDE WILL TALK IN
TERMS OF DATA HARMONIZATION.
THIS GETS TO THE STANDARDS.
THERE'S TOOLS THAT PROVIDE
AUTOMATED QUALITY REPORTS.
WE COULD TALK ABOUT THIS KIND OF
THING FOR A LONG TIME SO I WON'T
BUT THE KEY AND MESSAGE IS AS WE
ALL KNOW THERE'S LOTS OF
DIFFERENT VENDORS, E.H.R.
VENDORS OUT THERE.
THEY ALL KIND OF PROVIDE DATA IN
DIFFERENT WAYS AND DIFFERENT
INTERNAL FORMATS BY HARMONIZING
TO A COMMON DATA MODEL AND THE
EXISTING TOOLS YOU HAVE YOU CAN
GET SOME SUCCESS METRICS.
THAT'S WHAT YOU SEE THERE ON THE
RIGHT IS JUST THAT OVER TIME
MOST OF THEM GET GREENER AND
THAT PROCESS OF GETTING HIGHER
DATA QUALITY IS KIND OF A
CONTINUOUS JOURNEY.
FOR INSTANCE AS WITH THE
PANDEMIC THERE'S LOTS OF NEW
LABS EACH SITE WAS CREATED THEIR
OWN AROUND COVID AND LAB NAMES
AND RANGES AND THINGS LIKE THIS.
THINGS LIKELY DATA GOT WORSE FOR
A WHILE UNTIL WE HARMONIZED
THOSE LABS.
IT'S A JOURNEY WE CONTINUE ON.
THERE'S NOT A SET DESTINATION
THAT WE WILL NECESSARY ACHIEVE
BUT THE DATA SET GETS RICHER
OVER TIME AND MORE HARMONIZED
OVER TIME.
THIS TELLS YOU WHAT WE HAVE
RELEASED TO RESEARCHERS.
WE HAVE ABOUT 250,000 WHOLE
GENOMES.
ABOUT HALF OF THESE ARE NOT
EUROPEAN ANCESTRY AND YOU CAN
SEE THE OTHER COMPONENTS.
THE BIGGEST COMPONENT OF COURSE
ARE SURVEYS, ELECTRONIC HEALTH
RECORDS, 15,000 FITBIT RECORDS.
SOME OF THOSE GO BACK TO AROUND
2010.
WE HAVE UP TO 10 YEARS OR MORE
OF FIT BIT RECORDS THAT SHARED
THEIR OWN DATA AND THE HEALTH
RECORD GOES BACK TO 40 YEARS.
SOME OF THE DATA GOES BACK TO
1980.
AMONG THE 245,000 GENOMES WE
HAVE WE OBSERVED ABOUT A BILLION
VARIANTS AND ABOUT 600 MILLION
OF THOSE ACTUALLY ARE NOT IN
NOMAD, 150 MILLION OCCUR AND
THERE'S VARIATION LARGELY LINKED
TO ELECTRONIC HEALTH RECORDS TO
DO DEEPER ANALYSIS OF
PHENOTYPES.
THIS JUST GIVES YOU A SENSE OF
THE RANGE OF FITBIT DATA AND THE
KINDS OF THINGS WE HAVE IN
THERE.
WE JUST RELEASED SLEEP DATA.
THERE'S OTHER KIND OF DATA THAT
WE'LL BE RELEASING OVER TIME.
WE ALSO LEFT PEOPLE WITH OTHER
DEVICES THROUGH APPLE HEALTH
KIT, FOR INSTANCE.
THOSE DATA AREN'T AVAILABLE YET
IN THE WORK BENCH BUT WE
CONTINUALLY WORK TO HARMONIZE
AND RELEASE THOSE.
THIS IS A SENSE OF WHAT THE
BROWSER LOOKS LIKE.
WHAT YOU SEE THERE IS WHEN YOU
HIT SOMETHING LIKE CANCER IT
FOLLOWS THE TERMINOLOGY.
SO THE ICD9s AND 10s ALL GET
MAPPED UP TO A HIERARCHY AND SO
SOME OF THE SEARCHS YOU CAN DO
IN THE WORK BENCH WHEN YOU BUILD
TOOLS YOU CAN DO THINGS LIKE
PULL UP AND EVERYONE CAN PULL UP
A BETA-BLOCKER AND IT WILL GIVE
MEDS AND YOU CAN GO SPECIFICALLY
AND LOOK FOR SPECIFIC DRUGS IF
YOU WANT.
THIS IS THE RESEARCHER WORK
BENCH AND IT'S ALL GRAPHICAL AND
VERY MUCH LIKE THE U.K. BIO
BANKS R.A.P. PLATFORM.
WE LAUNCHED A LITTLE BIT EARLIER
SO WE HAVE MORE USERS AT THIS
POINT BUT WE HAVE THIS ANALYSIS
DONE IN OUR CLOUD.
RIGHT NOW WE SUPPORT PYTHON AND
SAS AND DOING IN TERM TESTING
AND LOOKING AT TOOLS AVAILABLE.
IT'S A PASSPORT ACCESS MODEL.
COME INTO THE RESOURCE AND AS
YOU ARE APPROVED AS A
RESEARCHER, YOU'RE ABLE TO DO AN
ANALYSIS, YOU'RE ABLE TO CREATE
NEW WORK SPACES ON THE FLY.
DON'T NEED TO DO SEPARATE IRB
APPROVALS.
ALL THE RESEARCH RIGHT NOW WE
HAVE AVAILABLE IS NON-HUMAN
SUBJECTS AND COVERED BY OUR
COMMON IRB AND HOPING TO WORK
WITH NOT FOR PROFIT
ORGANIZATIONS AND BEGINNING TO
ACCEPT INTERNATIONAL
APPLICATIONS.
THIS GIVES YOU A SENSE OF THE
APPLICATION PROCESS.
FIRST YOU CONFIRM THE
INSTITUTIONAL AGREEMENT.
AFTER THAT THERE'S ACCOUNT
VERIFICATION STEP AND WE
VERIFIED WHO YOU ARE AND
VERIFIED YOU HAVE A CONNECTION
TO THAT INSTITUTION.
USUALLY THAT'S THROUGH AN E-MAIL
ACCOUNT AT THE INSTITUTION.
THERE'S SOME MANDATORY TRAINING
EFFORTS AND THEN YOU CAN COME IN
AND SIGN OUR DATA USER CODE OF
CONDUCT AND THEN YOU'RE ABLE TO
GET STARTED.
YOU CAN -- IF YOU HAVE APPLIED
BRAND NEW DE NOVO THROUGH THE
PROGRAM YOU CAN MAKE IT THROUGH
ALL FOUR STEPS IN ABOUT TWO
HOURS AND START DOING WORK AND
HAVE STARTED YOUR FIRST PROJECT
IF YOU'RE INSTITUTION IS ALREADY
SIGNED ON.
MOST IN THE U.S. -- MOST
ACADEMIC INSTITUTIONS HAVE
ALREADY SIGNED ON.
SO WE'RE ABOUT 7,000
RESEARCHERS, ABOUT 580
INSTITUTIONS.
WE REALLY HAVE FOCUSSED JUST
LIKE WITH OUR PARTICIPANTS AND
ENGAGING UNDER REPRESENTED
RESEARCHERS AND NOT YOUR TYPICAL
R1 INSTITUTIONS AND 33% ARE
DIVERSE BY RACE AND ETHNICITY
INCLUDING ANY RACE OR ETHNICITY
SELF-DECLARED NOT WHITE OR AS
ASIAN.
WE HAVE ABOUT 40% OF HBCUs
SIGNED ON.
WE CONTINUE TO GO AFTER OTHER
MINORITY-SERVING INSTITUTIONS TO
HELP BRING THEM ON BOARD.
WE JUST FUNDED OUR MOST RESEARCH
AWARD ABOUT A WEEK AGO THAT WILL
HELP ENGAGE AMERICAN INDIAN,
ALASKAN NATIVE COMMUNITIES OF
RESEARCHERS AS WELL AND TRYING
TO THINK BROADLY ABOUT THE
ENGAGEMENT EFFORT.
THESE ARE SOME OF THE
PUBLICATIONS.
THERE'S LOTS OF TRACK RECORDS OF
OTHER PROGRAMS HERE.
IT'S NOT SURPRISING THAT MORE
THAN HALF THE PUBLICATIONS COME
FROM THIS YEAR.
IT TAKES A WHILE TO BUILD UP IN
TERMS OF THE PROCESS.
230 OR SO I THINK PUBLICATIONS
NOW THAT ARE USING OUR DATA.
HERE'S SOME EXAMPLES.
WE'RE STARTING TO GET GENOMIC
RESULTS MORE COMMONLY USED.
SOME OF OUR MOST IMPACTFUL AND
IF YOU LOOK AT MEASURES OF THAT
INCLUDE DEVICE DATA LIKE
FITBIT'S COUPLED TO DISEASE
STATUS AND CHANGE IN COVID AND
THINGS LIKE THAT.
SO PEOPLE HAVE DONE -- I'LL
MENTION THOSE STUDIES IN A
LITTLE BIT.
THIS WAS THE FIRST PAPER
PUBLISHED AND THERE WAS
REFERENCE TO MACHINE LEARNING
AND A.I. APPROACHES EARLIER.
AND THEY FIRST TRAINED US AS A
SINGLE SITE DATA.
SALLY BAXTER AND HER COLLEAGUES
AT UCSD AND FOUND THAT THAT THAT
WORKED WELL AT UCSD WAS APPLIED
TO ALL OF US AT AND RANDOM
CHANCE BUT WHEN WE TRAINED SOME
OF THEM ACROSS MULTI SITE,
MULTIE.H.R. VENDOR AND
GEOGRAPHIES AND INSURANCE STARTS
OF STATUSES SOME ARE IN EXCESS
OF 4.5 AND THE MODELS ARE SO
MUCH BETTER AND THEY POINT OUT
THE IMPORTANCE OF DIVERSE DATA
SETS AND I LIKE SEEING THAT IN
OUR DATA AND ONE THING WE MAKE
AVAILABLE.
THIS IS A PHEWAS LOOKING AT
HEREDITARY CANCER GENES.
MANY OF THESE ARE PART OF ACMG
RECOMMENDED RETURN AGAINST FOR
SECONDARY FINDINGS.
WE LOOK AT PATHOGENIC VARIANTS
IN THE 23 DIFFERENT GENES.
THIS FROM MY LAB.
YOUR LOOKING AT PHEWAS FOR THAT
GENE.
AND I KNOW IT'S HARD TO PICK OUT
BUT THE ORANGE ARE REPLICATION
AND GRAYS WOULD BE POTENTIALLY
NEW FINDINGS.
WHAT WE'RE ABLE TO FIND THROUGH
THE ANALYSIS THAT WAS ABLE TO
RAPIDLY HARMONIZE THE U.K. BIO
BANK AND CLINICAL COAND
U.S.-BASED SEQUENCE RESULTS INTO
A COMMON BACKBONE OF USING FEE
CODES OR THE PHEWAS-BASED
ARCHITECTURE TO PUT THE CODES
INTO ONE BACKBONE AND THE
PHENOTYPES WOULD BE HARMONIZED
VA
COULD BE VERIFIED QUICKLY AN LYF
THE ASSOCIATIONS AND IDENTIFIED
NEW ASSOCIATIONS IN TWO COHORTS
AND SO NEW CANCERS AND NEW
NON-NEOPLASTIC DISEASES AND
EXPANDS THE GENOME AND GIVES US
AFFECT SIZE AND MORE COHORT TO
THINK ABOUT THE HEREDITY DISEASE
RISK.
THIS IS A LARGE-SCALE GWAS DATA
AND GETS TO A MILLION EUROPEAN
ANCESTRY WITH BLOOD PRESSURE
READINGS.
THE FIRST ALL OF US GENOMIC SET
CAME OUT AND WE WERE ABLE TO
ADD A NUMBER OF PEOPLE FROM
AFRICAN ANCESTRY AND THERE WERE
OVER 100 NOVEL LOCI IT DID
PREDICT BLOOD PRESSURE IN PEOPLE
OF AFRICAN AMERICAN ANCESTRY BUT
WE'RE LOOKING AT A 13 MILLIMETER
MERCURY DIFFERENCE VERSUS A 5
MILLIMETER MERCURY OF DIFFERENCE
AND THE PREDICTION FOR
HYPERPENSION CHANGES AND IT
EMPHASIZES THE NEEDS TO DO THESE
ANALYSES IN DIVERSE POPULATIONS.
WE CAN SEE HERE THE
REPRODUCIBILITY OF THAT.
THIS IS A PHEWAS LOOKING AT STEP
COUNT AND INCIDENT DIAGNOSIS
OVER TIME.
YOU CAN SEE HERE THAT JUST NOT
SURPRISINGLY A WHOLE BUNCH OF
DISEASES ARE ASSOCIATED WITH
WALKING MORE.
SO WALKING MORE OR RUNNING MORE
PROTECTS AGAINST A BUNCH OF
DIFFERENT THINGS.
AND THEN WHAT I LIKED AS A
PRIMARY CARE PHYSICIAN WAS THE
STUFF ON THE RIGHT.
IT'S HARD TO GET SOMEONE GOING
FROM 2,000 STEPS A DAY TO
10,000.
YOU CAN SEE THERE'S A CONTINUOUS
SLOPE HERE AND IF YOU DOUBLE
FROM 2,000 TO 4,000 YOU MADE A
DIFFERENCE FROM MANY DISEASES
AND THE BEND IN THE CURVE IS
ABOUT 8,000 AND YOU HAVE
DRAMATICALLY REDUCED YOUR
DIABETES RISK IF YOU CAN GET TO
8,000 BUT IF YOU GET TO 6,000
YOU'VE DONE A LOT TOO AND IT'S
ACTIONABLE AND MEANINGFUL FOR
REAL PATIENTS IN THAT WAY.
THIS IS AN EXAMPLE OF THE POWER
OF HAVING THESE KINDS OF DATA
AVAILABLE AND IN COMMON DATA
MODELS.
THIS WAS MY LAST Ph.D. STUDENT
AT VANDERBILT BEFORE I MOVED TO
THE NIH AND USED MULTIPLE DATA
SETS AND FOUND DRUGS THAT MAY
LOWER CHOLESTEROL AND STUDIED AT
VANDERBILT ELECTRONIC HEALTH
RECORD TO LOOK AT WHEN ONES
WERE CHOLESTEROL.
YOU CAN SEE THERE'S AN EFFECT
AND PREDICTED BY THE GENE
EXPRESSION DATA WHICH IS WHAT WE
HOPE TO SEE.
YOU DID THIS AT VANDERBILT AND
TOOK A COUPLE YEARS TO BUILD
OUT.
CAN YOU DO THIS ANYWHERE ELSE?
HE TOOK HIS CODE AND VANDERBILT
MOVED TO THE OMOC COMMON DATA
MODEL THAT THE POINT AND HE
APPLIED TO ALL OF US AND IN A
WEEK AFTER HIS FIRST APPLICATION
WAS ABLE TO SEND ME A FIGURE
THAT LOOKED A LOT LIKE THIS ONE
THAT HAD THE PRELIMINARY RESULTS
OF THE SAME ANALYSIS IN ALL OF
US BY CUTTING AND PASTING THE OM
OC CODE WHICH WAS LONG AND THE
HARD WORK OF LOOKING THROUGH THE
MEDICATIONS AND PICKING WHAT
LABS TO CLEAN WAS PORTABLE
ACROSS DIFFERENT DATA SETS.
THIS STUDY LOOKS AT REPLICATION
OF KNOWN GENOMIC SIGNALS IN THE
U.K. BIO BANK.
IT'S IMPORTANT TO LOOK AT THE
COMMON AND RARE DISEASE TO SEE
THE AFFECT SIZE IS SIMILAR.
WHENEVER YOU RELEASE SOMETHING
NEW AND THERE'S A NUMBER OF
STUDIES THAT COME OUT LIKE THIS
NOW, IT'S HELPFUL TO SEE WHAT
YOU EXPECT TO SEE AND CAN DO THE
SIMILAR KIND OF ANALYSIS ACROSS
THE TWO DATA SETS COLLECTED IN
DIFFERENT WAYS.
EVERYONE IS OVER 18 AND THE U.K.
BIO BANK WAS 40 TO 69-YEAR-OLDS
IN A NATIONAL HEALTH CARE
SYSTEM.
SO THERE'S DIFFERENCES BUT
SIMILAR RESULTS OF THINGS WE
EXPECT TO FIND.
GETTING NEW DATA AND FILLING IN
THE HOLES IS IMPORTANT IN THE
UNITED STATES AS WELL AS MOVING
BEYOND ELECTRONIC HEALTH RECORD
DATA.
WE ASKED THINGS LIKE HOME AND
WORK ADDRESS.
WE HAVE THAT LOCATION DATA.
WE HAVE NOT COMPARED IT TO
ENVIRONMENTAL DATA.
WE HAVE OTHER BIO SPECIMENS WE
CAN DO THINGS WITH AND THIS
AWARD FOR ACQUISITION OF DATA IS
REALLY DESIGN TO GO GET DATA TA
WE CAN LINK IN AND USE PRIVACY
PRESERVING DATA LINKAGE METHODS.
WORKING WITH OTHER KINDS OF
CLAIMS AND HIE KIND OF DATA
LOOKING AT ENVIRONMENTAL DATA
AND OTHER KINDS OF RESOURCES WE
CAN CONNECT TO AND THEN
HARMONIZING WITH THAT AND
BRINGING THAT INTO OUR DATA SET
TO MAKE AVAILABLE AND THIS WILL
ALSO IMPORTANTLY BE A BIG
INVESTIGATION INTO ENVIRONMENTAL
DATA WHICH I THINK IS REALLY
IMPORTANT.
THEN BRIEFLY ON THE FUTURE THE
BIGGEST THING WE'RE WORKING ON
IS PEDIATRICS.
YOU HAVE TO BE 18 OR OVER TO
ENROLL RIGHT NOW AND WE'RE HARD
AT WORK FOR A PEDIATRIC PROGRAM
AND EXPECT SHORTLY WE'LL START
OUR VERY ALPHA TESTS OF
ENROLLING THE EARLIEST
PARTICIPANTS STARTING WITH THE 0
TO 4 RANGE AND THEN WE WILL MOVE
ON TO BUILD THE PROTOCOLS AND
SURVEYS CHANGE AND THE PROCESSES
ARE DIFFERENT WITH DIFFERENT AGE
RANGES AND BIO SPECIMEN
COLLECTION AND PROCEDURES AND
THINGS LIKE THAT ALL VARY.
WE ARE STARTING WITH THE
YOUNGEST PART AND WE'LL BE
GROWING THAT TO BE WHAT WE HOPE
WILL BE ONE OF THE WORLD'S
LARGEST PEDIATRIC COHORTS.
THERE'S MORE GENOMICS COMING IN
2024.
WE'LL HAVE ANOTHER DATA RELEASE
WHICH WILL TAKE US OVER 400,000
GENOME SEQUENCES AND PRODUCING
MORE LONG READS AND DIGITAL
TECHNOLOGY GIVING OUT ANOTHER
30K OR SO DEVICES TO PEOPLE AND
WE RELEASED AT LEAST ONE NEW
SURVEY A YEAR AND WE FOCUSSED ON
MENTAL HEALTH AND WE HAVE
MODULES THAT GIVE US
QUANTITATIVE AND COGNITIVE
FACTORS MUCH MENTAL HEALTH AND
HEALTH AND DISEASE OVER TIME AND
I DEVELOPED THE ANCILLARY
STUDIES AND ENVIRONMENTAL DATA
ONE WE'VE BEEN THINKING ABOUT
AND THERE'S A NUMBER OF OTHER
ASPECTS WITH THE PARTNERSHIP
ACROSS THE NIH.
THEY'RE PARTNERSHIPS THE NIH AND
OTHER INSTITUTES AND CENTERS
INCLUDING NHLBI HAS BEEN AN
IMPORTANT DISCUSSANT ON.
WITH THAT I'M DONE WITH MY
SLIDE.
I WANT TO PUT THE LAST ONE WHICH
I MAKE THE POINT OF JUST THE
HUGEST THING IS OUR THANK YOU TO
OUR PARTICIPANTS.
THESE ARE REAL PARTICIPANTS WITH
THE EXCEPTION OF MY DAUGHTER WHO
I'M LOOKING FORWARD TO BEING
ABLE TO ENROLL IN THE FUTURE.
WE HAVE A NUMBER OF IMPORTANT
DISEASES THAT IN OUR FAMILY
HISTORY THAT HAVE MOTIVATED ME
TO BECOMING A DOCTOR AND ALSO
WITH ONE OF MY CHILDREN AS WELL.
IT IS -- WE ARE PERSONALLY VERY
INVESTED IN THE PROGRAM AND
EXCITED TO SEE ITS GROWTH.
THANK YOU FOR THE OPPORTUNITY TO
SPEAK TO YOU AND I'M HAPPY TO
TAKE ANY QUESTIONS ASSUMING WE
HAVE TIME FOR IT.
>> FANTASTIC.
THANK YOU SO MUCH DR. DENNY.
THAT WAS SUPER EYE-OPENING AND I
THINK HELPED A LOT OF PEOPLE
FIGURE OUT HOW TO BEGIN TO GET
INVOLVED WITH ALL OF US.
I HAVE A QUICK QUESTION TO KICK
OFF THE Q&A AND WE HAVE A LOT OF
QUESTIONS IN THE Q&A BOX.
THE FIRST IS WHETHER THERE IS
ANY OBLIGATION FOR PEOPLE WHO
ARE WORKING WITH THE DATA TO
RELEASE THEIR CODE WHEN THE
PUBLICATIONS COME OUT SUCH THAT
OTHER PEOPLE CAN BUILD OFF IT.
>> SO EVERYTHING'S IN OUR CLOUD.
AS SUCH, IT COULD BE THE CASE.
THERE'S NOT A REQUIREMENT FOR
IT.
WE DON'T HAVE THE CAPABILITY
WHERE YOU CAN JUST MAKE IT
DISCOVERABLE TO THE WORLD BUT
YOU CAN SHARE IT WITH ANYONE.
WHAT I WOULD LOVE TO SEE HAPPEN
IS FROM THE OTHER ASPECT THAT
JOURNALS WOULD SAY, HEY, PUT
YOUR URL OR YOUR BLI OR
SOMETHING FOR YOUR WORK SPACE IN
THE PAPER AND WE HAVE TECHNOLOGY
ACCESSIBLE AND HAVE SEARCHABLE
WORK SPACES.
WE MADE A NUMBER OF FEATURED
WORK SPACES PEOPLE CAN COPY AND
USE.
I STRONGLY ENCOURAGE PEOPLE TO
SHARE THEIR CODE.
>> THANKS.
THERE'S A COUPLE QUESTIONS IN
THE Q&A THEY THINK WILL MAYBE BE
BETTER ANSWERED BY LATER -- AND
THERE'S A QUESTION ON
INVESTIGATORS.
ARE THERE OPPORTUNITIES NOW OR
COMING UP ON THE HORIZON?
>> THERE WAS SOME OPPORTUNITIES
THAT PASSED AND THERE'S A WHOLE
SET OF RO3s AND R21s SOME OF
THEM HAVE BEEN AWARDED BUT THAT
WAS A PARTNERSHIP WITH 27 OTHER
INSTITUTES AND CENTERS AND
OFFICES.
BASICALLY, A VERY BIG, BROAD NIH
COLLABORATION.
THE OTHER THING WE'RE SEEING IS
A REAL UPTICK IN GENERAL
APPLICATIONS INCLUDING ALL OF US
IN THEIR APPLICATION.
I WOULD SAY THE FIRST THING IS
ANY RO1 AND R21 ETCETERA YOU
WANT TO INCLUDE ALL OF US, DO
IT.
THERE'S NO REASON NOT TO.
THERE MAY BE FUTURE
ANNOUNCEMENTS.
EVEN IF I KNEW OF THEM YOU KNOW
I CAN'T TALK ABOUT THEM.
THERE MIGHT BE THINGS ON THE
HORIZON.
>> THAT'S AN EXCELLENT QUESTION
AND PEOPLE MAY NOT HAVE THOUGHT
ABOUT WRITING THE RO1 AND USE
THE DATA AND PAY FOR THE
ANALYSIS.
THAT WOULD BE TRUE FOR ALL
PAPERS AND META-ANALYSES AND
COMBINATIONS AND MACHINE
LEARNING AND A.I. AND
APPLICATIONS.
IF THEY WANTED MORE DATA
COLLECTED FOR SPECIMEN CAN YOU
COMMENT ON THE PROCESS?
>> THAT'S A GREAT ADDITION.
RIGHT NOW WE DO NOT HAVE
INDIVIDUAL ACCESS TO BIO
SPECIMENS.
IF YOU WANT ACCESS THAT'S NOT
SOMETHING YOU CAN REQUEST AS AN
INDIVIDUAL RESEARCHER.
IT'S SOMETHING WE'LL DO IN THE
FUTURE BUT WE'RE STARTING THE
PARTNERSHIPS WITH INSTITUTES AND
CENTERS.
SO THE I.C.s ARE THRIVING
DEVELOPMENT OF ANCILLARY STUDIES
AND THEY MAY -- LIKE NPH THE
NUTRITION FOR PRECISION HEALTH
IS GETTING ACCESS TO BIO
SPECIMENS.
IT WAS THROUGH A PROGRAM
SPONSORED BY THE COMMON FUND
THROUGH THE BIG SCALE PROGRAMS
THAT WOULD HAVE IT COMING AS AN
RFA THAT WOULD HAVE BIO
SPECIMENS.
>> PEOPLE CAN WRITE TO YOU OR
THE PROGRAM OR THE PROGRAM
OFFICER AND IF IT RISES TO
SUFFICIENT PRIORITY AND SOMEONE
WANTS TO PAY ENOUGH IT MAY
HAPPEN.
>> THAT'S TRUE.
>> FANTASTIC.
WELL, TO KEEP US RUNNING ON TIME
FOR THIS MORNING, WE'LL GO AHEAD
AND WRAP UP THE Q&A FOR THIS KEY
NOTE TALK AND THANK YOU,
DR. DENNY AND THERE ARE
QUESTIONS THAT REMAIN IN THE Q&A
THAT DR. DENNY CAN HOPEFULLY
ANSWER AND WE WILL HAVE ANOTHER
Q&A AT THE END OF THIS SESSION
FROM 12:10 TO 12:30.
I'LL INTRODUCE THE SPEAKER FOR
THE NEXT SESSION.
DR. PYARAJAN THE DIRECTOR FOR
THE CENTER FOR DATA AND
COMPUTATIONAL SCIENCES AT THE
VETERANS AFFAIR V.A. IN THE
BOSTON HEALTH CARE SYSTEM.
DR. PYARAJAN DIRECTS THE CENTER
AND THE GROUP IS RESPONSIBLE FOR
THE ACQUISITION, INTEGRATION AND
ANALYSIS OF THE CLINICAL
PATIENT-REPORTED AND MULTIOMICS
DATA FROM THE MILLION VETERANS
PROGRAM OR MVP.
WE'LL HEAR ABOUT ANOTHER
LARGE-SCALE DATA COLLECTION
INITIATIVE FOR LEARNING MORE
ABOUT POPULATION-SCALE HEALTH.
THIS IS ONE OF THE LARGEST
PRECISION MEDICINE PROGRAMS IN
THE WORLD.
WE HEARD ABOUT U.K. BIO BANK AND
THE "ALL OF US" INITIATIVE.
THIS IS ANOTHER INITIATIVE WHERE
LARGE DATA IS ACCESSIBLE FOR
PEOPLE TO WORK WITH.
SO WITH THAT I WILL HAND IT OVER
TO DR. PYARAJAN TO TAKE IT AWAY.
THANKS.
>> THANK YOU FOR THE NICE
INTRODUCTION.
IT'S GREAT TO BE HERE.
I'M BRINGING UP THE SLIDE DECK.
THANKS TO THE ORGANIZERS FOR
INVITING ME AND GIVING ME AN
OPPORTUNITY TO TALK ABOUT THE
MILLION VETERAN PROGRAM.
IN THE NEXT FEW SLIDES I'M
HOPING TO GIVE YOU A PREVIEW OF
WHAT WE'VE BEEN DOING IN THE
LAST 12 YEARS OR SO.
SO THE MILLION VETERAN PROGRAM
WAS INITIATED IN 2010 AND 2011
AS PART OF THE NATIONAL RESEARCH
PROGRAM AND BASICALLY AT THAT
TIME WAS DESIGN TO ADVANCE PRE
DECISION HEALTH CARE BY LEARNING
HOW GENES AND LIFESTYLE AND
EXPOSURE ESSENTIALLY AFFECTS
HEALTH AND WELLNESS.
EVERY PARTICIPANT SAY WILL HA
HAVE -- IS A VOLUNTEER AND GIVES
SPECIMEN AND ABLE TO INTEGRATE
THEIR AVAILABLE ON THEM IN THE
V.A. SYSTEMS, FOR EXAMPLE, AND
DEVICE SPECIFIC DATA.
OVER THE LAST 12 OR 13 YEARS
V.A. HAS INVESTED OVER $550
MILLION TO BUILD A COHORT FOR
CURATING THE HEALTH CLINICAL
DATA AND WE HAVE BUILT A
RESEARCH PLATFORM SIMILAR TO ALL
OF US IN THE U.K. BIOBANK AND IT
WILL BE MORE PRUDENT TO BRING
THE RESEARCHERS AND COMPUTING TO
THE DATA WITH MORE AND MORE.
AND WHAT WE DO IS WE SCHEDULE AN
APPOINTMENT WITH THEM IF THEY
ALREADY HAVE A HEALTH CARE
APPOINTMENT IN ANY OF THE V.A.
SPREAD ACROSS THE COUNTRY WE
SORT OF THERE'S AT LEAST 50
COLLECTION SITES ACROSS THE V.A.
AND THEY MEAN THAT MOVE FROM ONE
V.A. TO THE OTHER DEPENDING ON
THE CRITERIA YOU WANT IT TARGET
IN ANY CYCLE.
RECENTLY WE HAVE ALSO ADDED THE
ONLINE PROCESS WHERE THINGS LIKE
SIGNING CONSENT AND ANSWERING
THE SURVEY QUESTIONS CAN BE DONE
ONLINE.
AND ENROLLING IN A V.P. INVOLVES
THE FOLLOWING.
HAVE YOU A CONSENT PROCESS THAT
INCLUDES THEIR PERMISSION TO USE
DATA FROM THE MEDICAL RECORDS
AND OTHER DATA SOURCES.
THEY PROVIDE A SPECIMEN OF BLOOD
AT THE TIME OF ENROLLING AND
THEN COMPLETE A COUPLE SURVEYS
WE PROVIDE TO EVERY VETERAN THAT
JOIN AND I CAN TALK A LITTLE BIT
MORE ABOUT SURVEYS.
THEY'RE BASICALLY ONE IS A SHORT
SURVEY ABOUT FIVE PAGES AND IT
ASKS QUESTIONS ABOUT THE GENERAL
HEALTH AND THERE'S A LONGER
SURVEY ON PERSONAL HEALTH,
DISEASE AND NUTRITION, A LOT OF
NUTRITION AND ASSOCIATED
QUESTIONS.
THE SAMPLE COLLECTION.
ONE ISSUE WE THOUGHT OF VERY
EARLY IN THAT WE'LL BE
COLLECTING SAMPLES OVER A LONG
PERIOD OF TIME, TECHNOLOGY WILL
CHANGE AND IT'S BEING COLLECTED
FROM MULTIPLE SITES.
HOW DO WE MAKE SURE MAKE SURE
THE DATA IS HARMONIZED TO BEGIN
WITH.
ESSENTIALLY THE CRITERIA WE HAVE
IS THE VETERANS SHOULD BE THE
USER OF THE V.A. HEALTH CARE
SYSTEM.
THAT ENSURES THERE'S INFORMATION
ABOUT THEM IN THE AND IT'S
SHIPPED OVERNIGHT AND SENT BACK
TO THE CENTRAL LAB.
AND WE HAVE ALSO INCLUDED BEING
SELF-COLLECTION AT THE VETERANS
HOME.
ONCE THEY SHOW THEIR INTEREST IN
JOINING AND PARTICIPANTS WE SEND
THEM A SELF-COLLECTION KIT TO
THEIR HOMES THEY CAN COLLECT AND
USE THE KIT AND SEND IT BACK TO
OUR LABS.
MOST OF THE SAMPLES ARRIVE
WITHIN 24 TO 48 HOURS BUT WE
KEEP TRACK OF THE COLLECTION
TIMES AND WHEN IT WAS SHIPPED
AND RECEIVED AND PROCESSED IN
THE LAB WITHOUT CLINICAL
IDENTIFIERS.
THAT ALLOWS US TO DO THE
PREPARATIONS IN THE LAB LIKE
STORING THE MATERIAL WITHOUT
BIAS AND THE LAB DOESN'T KNOW
ANYTHING -- ANY OTHER FEATURES
OF EXAMPLES.
ONCE THEY MEET THRESHOLD THEY'RE
SENT OUT FOR MULTIPLE ANALYSIS.
SO WE HAVE COLLECTED OVER ALMOST
980,000 NOW AND CLOSING IN ON
THE MILLION.
HOPEFULLY IN THE NEXT FEW
MONTHS.
WE HAVE ESSENTIALLY BUILD THE
WORLD'S LARGEST GENOMIC DATABASE
LINKED TO A HEALTH CARE SYSTEM.
AND WE SORT OF RELEASE DATA IN
DIFFERENT TRANCHES FOR THE
GENOTYPE DATA FOR EXAMPLE.
WE HAVE OVER 650,000 DATA FROM
SAMPLES THAT WAS RELEASED LAST
YEAR.
SO, I WANT TO GIVE AN IDEA WHAT
THE M.V.P. DATA UNIVERSE IS.
YOU CAN IMAGINE THE CENTER IS
THE M.V.P. PARTICIPANT ITSELF.
EVERYTHING YOU SEE ABOVE IS
ACTIVELY COLLECTED AND
EVERYTHING BELOW IS PASSIVELY
COLLECTED AT THE TIME OF THE
PARTICIPANT SIGNING THE CONSENT
WE COLLECT THE SAMPLE AND HAVE
TWO SURVEYS THAT THEY COMPLETE
FOR INFORMATION THAT IS NOT IN
THE -- TO VALIDATE WHAT IS IN
THE E.H.R. OR NOT IN THE E.H.R.
AND FROM THE SAMPLES WE COLLECT
MOLECULAR DATA.
AND BELOW THE EQUATOR SO TO
SPEAK IS THE V.A. CLINICAL DATA.
SO IT HAS A SYSTEM DATING BACK
TO THE 1990s.
MORE THAN 30 YEARS OF
LONGITUDINAL DATA IS AVAILABLE
IN THE E.H.R. SYSTEM.
WITH YOU HAVE 23 MILLION FOLKS
OF WHICH ABOUT MILLION ARE
ACTIVE USERS OF THE SYSTEM AND
THE WAY WE HAVE IS AS THE
PROJECT GETS APPROVED WE GO AND
PULL DATA ON AN AS-REQUIRED,
PROJECT-BY-PROJECT BASIS AND
INCORPORATE LIKE THE INDEX AND
CMS AND HAVE DATA FROM THE
DEPARTMENT OF DEFENSE AND
EXPOSURE DATA.
THE DATA IS SORT OF A NESTED
DESIGN SO TO SPEAK.
WE REACH OUT TO FOLKS IN THE EMR
AS I SAID ONE OF THE REQUIREMENT
TO PARTICIPANT IN MVP IS THEY
HAVE TO BE USING THE HEALTH CARE
SYSTEM.
EVERYBODY IS IN THE EMR SYSTEM
AND THE SUBSET OF THEM RECOLLECT
THE SAMPLES WHO ARE PARTICIPANTS
AND WE COLLECT THE SAMPLES AND
RUN A CUSTOM GENOTYPING CHIP ON
ALL THESE SAMPLES.
THIS IS THE METRICS ARE NOW A
CHIP SIMILAR TO THE U.K. BIO
BANK CHIP FOR EXAMPLE.
AND STARTED DOING ETHNIC FOCUS
ARRAY TO CAPTURE MORE
INFORMATION FROM NON-CAUCASIAN
SAMPLES TO REACH THE GENOTYPE
DATA WE COLLECT FROM THE SAMPLE.
WE ARE CLOSING IN ON A MILLION
SAMPLES TO BE GENOTYPED.
THE LAST WAS ABOUT 650,000.
THE WHOLE GENOME WE COMPLETED
ABOUT 150,000 WHOLE GENOME THE
FIRST TRANCHE RELEASED ABOUT
10,000 AND IN THE PROCESS OF
RELEASING A SECOND TRANCHE THE
NEXT FEW MONTHS.
WE HAVE METHYLATION DATA ON
ABOUT 90,000 WE HAVE ABOUT
90,000 METABOLOMICS AND CURATE
AND RELEASE THE DATA AND WE HOPE
TO DO MORE OF THOSE IN THE
COMING YEARS.
AND WE JOKE IF YOU HAVE SEEN ONE
V.A. YOU HAVE SEEN ONE V.A.
SYSTEM HAS EVOLVED OVER THE LAST
30 YEARS.
AND NOT ONLY HAS IT EVOLVED
ACROSS ALL THE CENTERS BUT EACH
OF THE STATIONS IN THE 165 OR
MORE V.A. HOSPITALS HAVE THEIR
OWN DATA ELEMENTS THEY USE.
THEY HAVE A LITTLE BIT OF A
FREEDOM IN TERMS OF WHAT HOW
THEY RECORD THEIR DATA AS
MEDICATIONS, ETCETERA.
IT'S A CHALLENGE TO MAKE SURE
THAT WHEN WE DO --
>> YOU ACCIDENTALLY PRESSED YOUR
MUTE BUTTON.
>> OOPS.
SORRY, CAN YOU HEAR ME?
OKAY.
THANK YOU.
THERE WAS A QUESTION EARLIER
DEVELOPING THESE PHENOTYPING
ALGORITHMS.
ONE THING WE STARTED DOING FOR
EXAMPLE IS THAT ANY PHENOTYPE
THAT IS PUBLISHES WE KIND OF
CAPTURE THAT ALGORITHM AND THE
SENSITIVITY AND SPECIFICITY AND
THE METADATA ABOUT IT AND WE
HAVE STARTED BUILDING A
PHENOTYPE OF LIBRARIES SO TO
SPEAK.
SO AS I SAID I WAS AT THE
PREVIOUS SLIDE I WAS TALKING
ABOUT HOW WE ARE GENERATING ALL
THESE MULTIOMICS DATA.
WE DO THAT CENTRALLY AND THAT
ALLOWS US TO DEVELOP
STANDARDIZED DATA PROCESSING
PIPELINES SO WE HAVE SORT OF
STRICT QUALITY CONTROLS FOR
EXAMPLE THAT ALLOWS US TO HAVE
STRICT QUALITY CONTROLS HOW THE
DATA IS GENERATED, HOW THE DATA
IS PRE PROCESSED AND ALSO GIVES
US THE OPPORTUNITY TO GENERATE
AMPLE METADATA ABOUT THE DATA
ITSELF THAT CAN THEN ACCOMMODATE
THE DATA.
AS YOU CAN IMAGINE THE VOLUME
AND THE VARIETY OF DATA IS HUGE
IN M.V.P.
WE HAVE MORE THAN 30 PETABYTES
ALREADY AND COUNTING.
MOST OF OUR DATA INTEGRATION
HAPPENS ON AN AS-NEEDED BASIS
BECAUSE WE CAN PULL THE LATEST
AN GREATEST DATA FROM THE E.H.R.
FOR EXAMPLE WHEN A PROJECT
STARTS.
SO WE HAVE DEVELOPED A SYSTEM
THAT CAN INTEGRATE DATA ON AN
AS-NEEDED BASIS.
I'M NOT GOING TO TALK A WHOLE
LOT ABOUT IT BUT DATA BEING
VETERANS DATA THE SECURITY IS
PARAMOUNT TO US.
SO WE HAVE OVER THE LAST 12
YEARS HAVE DEVELOPED A
MULTIFUNCTIONAL SECURE
ENVIRONMENT.
AND COMPUTING TO THE DATA.
SO EACH STUDY HAS ITS OWN STUDY
SPACE AND WE HAVE MORE THAN 100
ANALYTICAL TOOLS AVAILABLE RIGHT
NOW IN THE PLATFORM.
TO GIVE YOU AN IDEA ABOUT THE
M.V.P. COHORT IT'S AVERAGE MALE
MOST AVERAGE 67 AND MOST THE
POPULATION IS NOT CAUCASIAN
WHITE AND SOME RARE
SELF-REPORTED ANCESTRIES LIKE
THE NATIVE HAWAIIAN IS .47% OF
OUR COHORT.
SO THIS IS AN EXAMPLE WHERE WE
CONTROL THE DATA QUALITY.
FOR EXAMPLE, THIS IS THE SAMPLE
ACROSS 400 AND THE THIRD RELEASE
OF 450,000 SAMPLES WHERE THE
MEAN RATE IS 99.36%.
IF THOSE STRICT CONTROL MEASURES
ARE NOT MET WE REASSESS.
THIS ARE SOME OF THE
OBSERVATIONS WE HAD EARLY ON
WHERE WE COMPARED THE FREQUENCY
OF THE M.V.P. DATA SET WITH
1,000 GENOMES.
YOU CAN SEE THE M.V.P. CAUCASIAN
FREQUENCIES IS VERY COMPARABLE
TO THE GENOME AND THE OTHERS
DON'T COMPARE.
AND THIS BASICALLY POINTS US TO
THE SAMPLES BEING MORE REAL
WORLD DATA AND A DIVERSE COHORT
OF TENTATIVE ANCESTRY.
MULTIPLE ANCESTRY.
WE'RE SHOWING THE M.V.P. IN ONE
AND TWO PROJECTED ON TO THE
POPULATION AND THE M.V.P.
POPULATION IS DIFFERENT.
AND THIS LEADS TO SOME ISSUES
AND CAUTIONARY TALES HOW DATA
SHOULD BE ANALYZED AND
INTEGRATED.
SO WE HAVE THE GENOTYPE DATA WE
GENERATE FROM THE AND HAVE
REPEATED IT TO MULTIPLE
BACKBONES AND REFERENCE THE
GENOME AND THIS IS WHAT I'M
SHOWING AS THE QUALITY OF THE
TOP MED REFERENCE GENOME.
THE EUROPEAN, AFRICAN AND
HISPANIC SAMPLES WE HAVE THE Y
AXIS FOR EACH OF THE FREQUENCY
BUCKETS ON THE X AXIS BUT THE
ASIAN FOR EXAMPLE IS LAGGING
BEHIND IN QUALITY.
SO THIS IS WE PROVIDE IT TO BE
CAUTIOUS ABOUT IT.
I JUST PUT THIS TO SHOW ONE OF
THE PAPERS PUBLISHED IN 2020 AND
M.V.P. ENABLED BECAUSE OF THE
METADATA WE PROVIDE AND THE
STANDARDIZED DATA QUALITY WE
HAVE FOR M.V.P., FIRST OF ALL
THE LARGE PROVIDES LARGER
ANALYSIS AND THIS GENERATES AND
A LARGE NUMBER OF NEW
ASSOCIATIONS.
THEY WERE ABLE TO DO FOR EXAMPLE
ANCESTRY SPECIFIC ANALYSIS ON
ITS OWN TO IDENTIFY ANCESTRIES
SPECIFIC ASSOCIATIONS.
THIS IS AN EXAMPLE WHERE ONE OF
THE EARLY PUBLICATIONS IN 2018
WHERE WE DISCOVERED 118 NEW
GENOME LOCI ASSOCIATED WITH
LIPIDS.
AT THIS FIGURE I JUST PUT IN
THERE TO SHOW AND WE LOOKED AT
THE FREQUENCIES AS WELL AS THE
ESTIMATES AND ESTIMATES FOR THE
VARIANTS WHEN WE COMPARE IT TO
ON THE LEFT IS THE AFRICAN AND
ON THE RIGHT IS HISPANIC VERSUS
CAUCASIAN.
THERE'S A BETTER ALIGNMENT WITH
THE HISPANIC SAMPLES AND THE
CAUCASIAN SAMPLES IMPLYING MORE
GENOME ELEMENT IN HISPANICS THAN
AFRICAN.
WE'RE INTERESTED IN DOING
DISCOVERIES AND TRANSLATING TO
CLINICAL QUCARE.
IN A PAPER WE LOOKED AT THE HLA
FREQUENCIES THAT HAVE LARGE
PHARMACO GENOMIC AFFECTS ACROSS
MULTIPLE ANCESTRIES.
THERE'S QUITE A DIFFERENCE FOR
SOME OF THESE AND WE CAN PROVIDE
CLINICAL INTERPRETATION NOT JUST
AT THE POPULATION LEVEL BUT
ANCESTRY LEVEL.
WE HAVE MORE THAN 100 PROJECTS
WITH 700 RESEARCHERS ACTIVELY
WORKING WITH THE DATA.
WE HAVE ABOUT 400 PUBLICATIONS
THAT HAVE COME OUT -- OUR FIRST
WAS IN 2015 TO ABOUT EIGHT
YEARS.
I WANT TO THANK EVERYONE
INVOLVED IN THE OFFICE OF
RESEARCH AND DEVELOPMENT AND THE
PROGRAM OFFICE.
THANKS AGAIN.
I REALLY APPRECIATE THE
OPPORTUNITY AND THIS TIME I'LL
TAKE A FEW QUESTIONS.
>> WE HAVE ONE QUESTION THAT'S
COME UP FROM MULTIPLE ATTENDEES
WHICH IS HOW CAN NON-V.A.
RESEARCHERS ENGAGE WITH THE
DATA?
>> SURE.
SO RIGHT NOW THE WAY WE ARE
DOING IS WE ARE ENCOURAGING THE
RESEARCHERS TO FORM CONSORTIA.
THERE'S A V.A. RESEARCHER THEY
CAN APPLY FOR THESE AS A
CONSORTIUM AS THE LED FOR THE
PRIMARY P.I.
WE ARE TALKING AND STAY TUNED
WE'RE MAKING THE DATA AVAILABLE
MORE WIDELY TO NIH AND.
THERE'S
>> THERE'S A POLL COMING UP AND
THERE'S A QUESTION ABOUT THE
WORK BENCH.
AND THE QUESTION IS FOR ALL THE
DIFFERENT PROGRAMS, U.K. BIO
BANK, ALL OF US AND HOW DO THE
WORK BENCHES COMPARE?
DO THEY HAVE COMMON
FUNCTIONA
FUNCTIONAL
FUNCTIONALITIES.
>> AS JOSH WAS MENTIONING THE
U.K. BIO BANK AND THE ALL OF US
WORK BENCH ARE SIMILAR.
BUT THE M.V.P. WORK BENCH HAVE
WE TOOK A DIFFERENT APPROACH
BECAUSE WE WANTED TO MAKE IT
MORE EASIER FOR PEOPLE TO BRING
THEIR TOOLS TO DO THE ANALYSIS
OUR WORK BENCH IS A LITTLE BIT
DIFFERENT.
AP WE HAVE DATA BEEN APPROVED
FOR.
AND THEY CAN SHARE TOOLS AN ANY
PIPELINES THEY HAVE DEVELOPED
THEY CAN SHARE WITH OTHER STUDY
PERSONNEL ACROSS THE PLATFORM.
OUR PLATFORM IS DEVELOPING
TOOLS, MAKING TOOLS AVAILABLE TO
OTHERS AND THE ONLY THING STRICT
WITHIN THE STUDY IS THE DATA
THAT YOU APPROVED.
>> WONDERFUL.
THANKS SO MUCH.
FOR TIME SAKE WE'LL MOVE ON TO
ANOTHER SPEAKER AND WE'VE BEEN
HEARING FROM DR. CORESH AS A
CONFERENCE CO-CHAIR BUT I'LL
BRIEFLY INTRODUCE HIM AS THE
GEORGE W. COMSTOCK PROFESSOR OF
BIO MEDICINE AT JOHNS HOPKINS
UNIVERSITY WHO WILL SOON BE AT
NYU AND HE HAS OVER 150 MILLION
PROTEOMIC MEASURES.
DR. CORESH IS AMONG THE MOST
CITED RESEARCHERS HAVING
CO-AUTHORED ARTICLES CITED OVER
100,000 TIMES.
SO IT WILL BE A THRILL TO HEAR
FROM DR. CORESH ABOUT THE ARIC
STUDY AND WITH THAT PLEASE TAKE
IT AWAY.
>> WONDERFUL.
IT'S A PLEASURE AND NICE TO SEE
ALMOST 200 ATTENDEES AND TALK
WITH THE ARIC STUDY RENAISSANCE
IN AN AGING COHORT.
AGING WITH THE COHORT AN I
INTRODUCED THE CONCEPT OF A
RESEARCH STUDY AND ARIC SAY
COHORT STUDY HOW WE GET INSIGHTS
FOR AN ACTION.
ARIC WAS INITIATED IN 1987 AND
TALKED ABOUT THE FACT IT WAS
INITIATED BEFORE HE WAS BORN
SAID DR. WALKER AND YET USING
THE DATA.
I WAS BORN BEFORE 1987 BUT I WAS
A STUDENT AND ON THE LEFT IS A
CHAIR OF THIS STEERING COMMITTEE
FROM THE BEGINNING AN RICHIE IS
87 AND STILL AN ACTIVE
INVESTIGATOR HIMSELF.
YOU SEE IT'S A PARTNERSHIP
BETWEEN INVESTIGATORS, STAFF AND
PARTICIPANTS.
AND I WANT TO TALK ABOUT MODERN
TOOLS AN I'LL SHOW RESULTS.
SO ARIC HAD A COMMUNITY
COMPONENT.
IT'S INTERESTING TO THINK HOW
YOU CONNECT COMMUNITY, A COHORT
AND ACTUALLY NOW WE HAVE DONE A
CLINICAL TRIAL IN A WAY MUCH
SMALLER THAN THE U.K. BIO BANK
OR ALL OF US AND VETERANS AND
STILL LARGER THAN WHAT YOU CAN
DO IN ONE SITE.
IT'S BRIDGING THE DIFFERENT
LEVELS OF COLLABORATION.
THE ORIGINAL ARIC DESIGN HAD
THREE VISITS THREE YEARS A PART
AND IT'S BEEN ADOPTED BY THE
LEADING NHLBI COHORTS INCLUDING
MESA, STUDY OF LATINOS AND WE
STARTED WITH ABOUT 15792
PARTICIPANTS IN JACKSON,
MISSISSIPPI AND THE NOVEL EXAM
WAS A CAROTID INTERMEDIATE
INTERMEDIAL THICKNESS AND IT'S
IMPORTANT TO HAVE THE FUTURE
BANKING EFFORTS.
THAT BECOMES A SMALL PART OF THE
STUDY.
WHEN YOU LOOK AT THE WHOLE DATA
COLLECTION NOW WE'VE JUST BEEN
FUNDED FOR VISIT 11.
WE ARE AT VISIT 10 IN 2023 AND
OF THE 5200 PARTICIPANTS ALIVE,
WE'RE DOING SURVEILLANCE ON 4800
BUT SEEING IN CLINIC BECAUSE OF
COVID AND OTHER REASONS CLOSER
TO 1750 PARTICIPANTS.
SO I THINK THE IDEA OF FOLLOW-UP
RATES AND COMPLETENESS AND
INCOMPLETENESS AND HOW YOU DO
VAL IS INFERENCES IS IMPORTANT.
AND THE DATA COMPONENTS HAVE
INCREASED A LOT.
WE STARTED WITH A HUGE EMPHASIS
ON VASCULAR RISK FACTORS AND
MARKERS.
AND HOMO -- HOMEOSTASIS WAS
HARDER THAN THEY THOUGHT AND
STARTED WITH CALCIUM AND NEW
BRAIN MRI AND PET.
THE BRAIN MRI AND PET ARE IN
SUBSETS SO THAT'S AN IMPORTANT
THING AND IN THE TALK OF HOW TO
EXPAND SUBSET TO WHOLE GROUPS IS
INTRIGUING.
INTERESTINGLY WE HAVE COGNITIVE
FUNCTION TEST EARLY ON BUT IT
USED TO BE THREE QUESTIONS,
THREE MINUTES WORTH OF DATA AND
NOW WE DO AN HOUR'S WORTH OF
DATA WHEN WE DO THE FULL
BATTERY.
AMAZINGLY THE THREE MINUTES GETS
YOU QUITE A BIT AND WE CAN GET
TO UNDERLYING VARIABLES IN
COGNITIVE GLOBAL COGNITION FROM
EACH ONE OF THE TESTS WITH
VARIABLE RELIABILITY.
WE NOW DO INFORMANT INTERVIEWS
FOR ACTIVE DEMENTIA
SURVEILLANCE.
IT'S IMPORTANT PEOPLE WITH
DEMENTIA DO NOT COME TO VISITS.
IF YOU WAIT TO MAKE DIAGNOSIS AT
VISITS YOU WON'T GET IT.
YOU HAVE TO DO ACTIVE
SURVEILLANCE AND IN THE MISS THE
CASES AND AS WE'VE BEEN TOLD BY
DR. BOULWARE YOU HAVE TO BE
AWARE AND MAKE EFFORTS BECAUSE
OTHERWISE YOU'RE GOING TO GET
VARIABLE ASCERTAINMENT BY RACE
AND SOCIAL DETERMINATES OF
HEALTH.
WE INTEGRATED HEARING LOSS INTO
THE STUDY AND I'LL TELL YOU
ABOUT SOME MAJOR RESULTS FROM
QUANTITATIVE HEARING AND A NEW
CLINICAL TRIAL AND WE INTEGRATED
PHYSICAL FUNCTION ASSESSMENT AND
LOOKED AT LABS AND BIO BANKING
WITH A MAJOR FOCUS ON PROTEOMICS
WITH THE PLATFORM AND YOU HAIRED
FROM KEENAN WALKER AND I'LL TALK
ABOUT OTHER FINDINGS AND WE HAVE
A NEW EMPHASIS ON BLOOD
ALZHEIMER'S DISEASE AND NEURAL
DEGENERATION BIOMARKERS.
IT'S AN EVOLVING FASCINATING
FIELD AND THERE'S STUDIES ON
CANCER ON THE LIQUID BYPASS
CONCEPT FOR CANCER.
SO THIS IS A DIAGRAM OF ARIC.
YOU'LL NOTICE THERE WERE NO
VISITS FOR GREATER THAN 10 YEARS
AND NIH WAS CONSIDERED ENDING
THE FUNDING OF ARIC AND
DECREASING THE FUNDING FOR A LOT
OF THE COHORTS.
CLICK AGAIN.
YOU'LL SEE NOW
INVESTIGATOR-INITIATED PROPOSALS
ALLOWED US TO UNCOVER THE
INCREASING VALUE OF THE COHORT.
WE HAVE MORE VISITS AND MORE
DATA COLLECTION THAN EVER
BEFORE.
SO THERE'S ACTUALLY A SYNERGY OF
NEW DATA IN A RICH CONTEXT.
I THINK THAT'S SOMETHING WE NEED
TO THINK ABOUT IN THIS WORKSHOP
ABOUT BRIDGING AND CONNECTING
HOW DO YOU INCREASE THE VALUE OF
DATA.
NOT JUST BIG BUT VALUABLE.
CONNECTED, INSIGHTFUL,
INFORMATIVE AND CHANGING
PRACTICE AND POLICY.
LIKE EVERYBODY ELSE WE'RE
ENAMORED OF WEARABLES AND NOW
THE CHALLENGE IS SUCCEEDED HOW
WILL WE MANAGE SIX DEVICES?
THERE'S GOING TO BE TWO
DEDICATED SLEEP DEVICES OF SLEEP
PROFILE AND WATCH AND A PHYSICAL
ACTIVITY DEVICES AND THE LINK
AND THE COMMERCIAL GRADE DEVICE
THE FITBIT CHARGE.
THEY HAVE PEOPLE WEAR FOR FOUR
YEARS FOREVER.
WHY SHOULD WE STOP?
THIS IS MARRYING THE TYPES OF
THINGS WE'VE DONE ON SMALL SCALE
TO A LARGE SCALE WITH THE ADDED
CHALLENGE PARTICIPANTS ARE 80 TO
100.
HOW DO YOU GET A 90-YEAR-OLD TO
WEAR A FITBIT AND CHARGE IT AND
GET THE DATA TO US?
THE OTHER THING THAT'S
FASCINATING IS AS WE'VE DONE THE
DESIGN THERE'S A NEW DEVICE.
THEY DISCONTINUE THE LINK AND
START THE NEWEST DEVICE CALLED
LEAP.
IT GETS TO VALIDITY.
IF YOU CLICK, MY BIG QUESTION IS
WHAT IS WHAT THEY SAY THEY
CAUGHT.
ACTIVITY THEY'VE BEEN DOING FOR
A WHILE BUT WE KNOW ACCEL
ACCELEROMETRY IS DIFFERENT AND
THERE'S ISSUES WITH ACTIVITY
VERSUS INACTIVITY AND TRANSITION
AS A POWERFUL THING.
IT SAYS IT MEASURES SLEEP.
IT'S ON YOUR WRIST.
IS IT AS GOOD AS THE
RESEARCH-GRADE SLEEP DEVICE.
HEART RATE IS PROBABLY GOOD BUT
WHEN I USE A FITBIT AND GO ON A
SPIN CLASS IT TAKES FIVE MINUTES
TO RECORD AND THEY DO
INTERESTING THINGS AND WE HAVE
TO BE AWARE OF WHAT WE'LL GET.
HEART RATE VARIABILITY AND SKIN
TEMPERATURE AND BLOOD PRESSURE.
I SPENT A YEAR TRYING TO
EVALUATE THE DEVICE THAT WAS FDA
CLEARED FOR BLOOD PRESSURE AND
FOUND OUT REALLY WHAT IT WAS
DOING WAS PARITY HEART RATE WITH
A CALIBRATION OF BLOOD PRESSURE.
WE NEED TO KNOW WHAT MEASURES
ARE VALID AND IF WE CAN GET
BLOOD PRESSURE IN REAL TIME FROM
THESE DEVICES, IT WOULD BE
AMAZING AND TRANSFORMATIVE
BECAUSE BLOOD PRESSURE IS SO
IMPORTANT AND YET I WILL KIND OF
TRUST BUT VERIFY A LOT BEFORE I
BELIEVE THOSE SIGNALS.
NEXT SLIDE.
YOU HAVE TO BE FLEXIBLE WITH
OLDER AGE PARTICIPANTS.
WE CAN HAVE BASICALLY A
SEVEN-HOUR ONE DAY WHICH WE
THINK IS TOO LONG.
WE SUGGEST PARTICIPANTS DO TWO
DAYS AND THAT WOULD BE ABOUT
THREE TO OUR HOURS EACH DAY OR
THE PARTICIPANT MAY SAY I DON'T
WANT TO DO SEVEN HOUR OR TWO
DAYS, I'M ONLY WILLING TO GIVE
YOU HALF A DAY OR TWO HOURS OR
ONLY ANSWER THINGS ON THE PHONE.
SO WE HAVE TO BE FLEXIBLE TO GET
HIGH RESPONSE RATES PARTICULARLY
WHEN PREME OLDER AND HAVE
DISEASE.
THINK THERE IS AN INTERESTING
TENSION BETWEEN LET'S STUDY THE
WELL WILLING TO WEAR FOUR
WATCHES AND SEVEN ELECTRODE AND
LOVE DATA VERSUS THE CHALLENGES
OF PEOPLE ARE OLDER AND CHRONIC
DISEASE AND MAY HAVE DEPRESSION,
OTHER CHALLENGES, FOOD
INSECURITY AND YET THEY DEVELOP
THE DISEASES WE CARE ABOUT AND
NEED TO INCLUDE THEM.
WE'RE DEVELOPING A WHOLE
COMPONENT ON CAREGIVING, END OF
LIFE AND TRAINING FOR THE
INTERVIEWERS IN HOW TO BE
EMPATHETIC AND SUPPORTIVE OF
PARTICIPANTS.
NOW I'D LIKE TO SWITCH THE
RESULT.
METHODS ARE IMPORTANT.
I'VE BEEN VERY IMPRESSED WITH
THE BIG STUDIES AND THEIR
RESULTS.
I THINK IT'S ACTUALLY A
CHALLENGE AT LATEST FOR ME IN
HOW TO PROCESS THE VOLUME AND
CAPABILITY OF RESEARCH OF THINGS
LIKE U.K. BIO BANK AND ALL OF US
AND MILLION VETERANS PROGRAMS.
IT'S BEEN AN EXCITING PROGRAM
AND WE ALL NEED TO THINK OF HOW
TO DO BETTER.
I WANTED TO SHOW YOU THREE OR
FOUR EXAMPLES.
EXAMPLES OF COLLABORATION.
THIS IS ON THE LEFT A TRAINEE OF
MINE FOR ABOUT 10 YEARS AN NOW A
LEADING AUTHORITY ON RESEARCH ON
OBESITY AND HEART FAILURE
WORKING ONE THE AMERICAN HEART
ASSOCIATION LEADING THE CENTER
AND A POST-DOCTORAL RESEARCH
FELLOW ABOUT TO START A
RESIDENCY AND THIS IS HER
PROJECT.
IT ADDRESSES THE ISSUE OF THE
PROTEOMIC ALTERATIONS IN OBESITY
AND WE FIND OBESE INDIVIDUALS
HAVE ALTERATIONS AT THE
SIGNIFICANCE LEVEL OF 700
PROTEINS.
WE HAVE THE CHALLENGE OF HOW DO
YOU INTEGRATE THAT.
WE WANTED TO LOOK AT HEART
FAILURE AND SHE FOUND 19 OF
THESE PROTEINS RELATED TO HEART
VEIL YOU'RE RISK.
IF YOU LOOK ON THE RIGHT, YOU
SEE THE CONCORDANCE ON THE X
AXIS THE ASSOCIATION WITH
OBESITY AND ON THE RIGHT THE
ASSOCIATION WITH HEART FAILURE
AND THERE'S A CORRELATION.
NOT PERFECT BUT THING IN RED
HAVE AN INCREASED LEVEL IN
OBESITY AND INCREASED LEVEL FOR
RISK OF HEART FAILURE 10 YEARS
OR MORE LATER.
IN BLUE HAVE LOWER LEVELS IN
OBESITY AND ARE ACTUALLY
ASSOCIATED WITH A LOWER RISK OF
HEART FAILURE.
OVER ALL IT ESTIMATED 72% OF OF
THE EXCESS HEART FAILURE RISK
MAY BE EXPLAINED BY THE PROTEOME
WE'RE MEASURING.
EXCITING AND INTERESTING AND
THEN WE HAVE TO TAKE NEXT STEPS
HOW THAT WOULD CHANGE OUR
UNDERSTANDING OF BIOLOGY AND
MOVE TO EXPERIMENTAL SYSTEMS AND
THINK OF POTENTIAL THERAPEUTICS.
I WANTED TO TALK ABOUT KIDNEY
DISEASE.
IT'S BEEN MY PERSONAL PASSION TO
LOOK AT KIDNEY DISEASE AND
KIDNEY FAILURE AND END STAGE
RENAL DISEASE REQUIRING DIALYSIS
OR TRANSPLANTATION HAS BEEN AN
EPIDEMIC AND RATES UNACCEPTABLY
HIGH IN THE UNITED STATES AND
OTHER COUNTRIES.
WE'VE COME TO APPRECIATE ABOUT
10% OF PEOPLE IN THE U.S. HAVE
CHRONIC KIDNEY DISEASE.
WE TOOK THE ARIC DATA AND
LEARNED METHODS FROM GENOME-WIDE
ASSOCIATION STUDIES OF HOW TO
LEVERAGE THAT INTO A CKV
PROGNOSIS CONSORTIUM.
A CO-P.I. OF THE INITIATIVE IS
DIRECTOR OF PRECISION MEDICINE
AT NYU.
YOU CAN SEE THE CONSORTIUM HAS
GROWN.
WE'RE NOW ON RENEWAL.
WE HAVE 170 COHORT, 38 MILLION
AND THERE'S GEISINGER AND YOU
CAN SEE INFORMATION AT THE
WEBSITE.
WE TRIED TO CONTRIBUTE DATA TO
GUIDELINES AND THE HEAT MAP WITH
HIGHER RISK AND LOWER GFR AND
YOU SEE ON THE RIGHT INDICATED
BY MORE COLOR THAT'S RED AND
SUGGESTING THAT PATIENTS SHOULD
BE SEEN MORE OFTEN AT HIGH RISK
SAY CONCEPT WE HELPED DERIVE
BASED ON DATA IN 2009 TO 2013.
WE JUST RE-EXAMINED THIS IN
CREATININE AND IT WAS CONFIRMED
ACROSS OUTCOME AND THIS PAPER
SHOULD BE COMING OUT SHORTLY.
AND IT CITED IN THE GUIDELINES
WHICH HAVE BEEN FOR PUBLIC
COMMENT AND FILED IN 2024.
SO HOW DO YOU INDIVIDUALIZE
THIS?
SO THE HEAT MAP CONCEPT IS THE
WAY WE THINK OF RISK FACTORS.
LOWER GFR, HIGHER RISK.
TO INDIVIDUAL YOU NEED A RISK
SCORE WITH MORE VARIABLES.
SO WE'VE NOW PUBLISHED MORE THAN
HALF A DOZEN DIFFERENT RISK
SCORES.
GO TO ARE CDPK RISK TO LOOK AT
THEM AND MORE IMPORTANT THAN
KIDNEY DISEASE ITSELF IS THE
CONCEPT.
PEOPLE ARE DEVELOPING A.I.,
MACHINE LEARNING RISK SCORES AND
THEY'LL INDIVIDUALIZE AND WE
TESTED AND DEVELOPED THEM IN
DOZENS OF COHORTS AND VALIDATED
IT IN DOZENS OF COHORTS.
WE LEARNED IT'S IMPORTANT TO
TARGET THE POPULATION.
EACH RISK SCORE THE ARROW STARTS
IN A DIFFERENT PLACE.
YOU CAN LOOK AT THE RISK OF
KIDNEY FAILURE AND DONORS.
THEY'RE HEALTHY ON AVERAGE AGE
40.
YOU NEED TO LOOK AT ESTIMATING A
30 TO 40 YEAR RISK IN HEALTHY
PEOPLE THAT'S VERY LOW BECAUSE
DONORS SHOULDN'T HAVE A HIGH
RISK AND THE EQUATION IS FOR
PATIENTS WHO ALREADY HAVE KIDNEY
DISEASE AND KNOWING THE RISK OF
GETTING KIDNEY FAILURE AND
DIALYSIS VERY DIFFERENT.
WE ALSO UNDERSTAND THAT IN
DIFFERENT SETTINGS THERE'S
UNEXPLAINED RISK THAT'S QUITE
SIGNIFICANT AND WE NEED TO
UNDERSTAND THAT.
SO THE TIME HORIZON AND OUTCOME
AND THRESHOLD AND IMPORTANTLY
THE DECISIONS YOU NEED TO MAKE.
SO WHETHER OR NOT YOU DECIDE TO
START SOMEBODY ON A
MULTI-SPECIALTY CLINIC WHETHER
OR NOT YOU HAVE HIGH RISK OF
BREAST CANCER AND DECIDE TO DO
AAS EXTREME AS SURGERY TO REMOVE
THE BREAST AND SOME THOUGHT HOW
TO USE THE ALGORITHMS BROADLY
AND SPECIFICALLY AND WE LEARNED
IN NEW ARTICLES IF WE REALLY
WANT TO DO A FULL BENEFITS
ANALYSIS WE DON'T HAVE ENOUGH
DATA FOR THAT.
FINALLY I WANT TO TALK ABOUT
COGNITIVE DECLINE IN THE LAST
COUPLE EXAMPLES.
A THREAT TO AN AGING PLANET.
ALZHEIMER'S DISEASE AND VASCULAR
DEMENTIA.
ARE THEY COMPLETELY DIFFERENT?
WE THINK OF ALZHEIMER'S AS BEING
NON-PREVENTIBLE.
WE KNOW VASCULAR DISEASE IF WE
DID THE RIGHT THINGS MORE THAN
80% IS PREVENTIBLE AND WHAT
ABOUT DEMENTIA AND THE
CONNECTION.
IN STARTING TO DO THIS RESEARCH
WE WORKED WITH ALONZO AND
REALIZED THE VASCULAR RISK
FACTORS CONTRIBUTE SUBSTANTIALLY
TO THE RISK OF DEMENTIA BUT THE
ASSOCIATIONS WERE MEASURED AT
MID LIFE THAN OLDER AGE.
THIS IS COUNTER INTUITIVE.
IF YOU DEVELOP DEMENTIA AT 75
AND DIABETES GIVES A RISK OF
2.7, 25 YEARS LATER WHERE
DIABETES THAT DEVELOPS THAN 60
GIVES A LOWER AMOUNT OF RISK AND
SAME FOR HYPERTENSION.
WE NEED LONG FOLLOW TO
UNDERSTAND LIFE LONG DISEASE.
IT'S EXCITING TO HAVE A WEARABLE
BUT ANYTHING THAT CAN CHANGE IN
FIVE MINUTES HAS LIMIT TAKES FOR
PREDICTING -- LIMITATIONS FOR
PREDICTING HAS RISK DETERMINING
I'LL BECOME DEPARTMMENTED.
THIS SHOWS BRAIN AMYLOID IS
ASSOCIATED WITH MID LIFE
VASCULAR RISK FACTORS.
THIS SHOWS NOT ONLY IS
HYPERTENSION AND DIABETES
CONNECTED TO DEMENTIA IT CONNECT
TO THE AMYLOID DEPOSITION IN
ALZHEIMER'S DISEASE.
SUGGESTING THAT VASCULAR DISEASE
TREATMENT WOULD IMPROVE NOT JUST
VASCULAR DEMENTIA BUT ALSO THE
DEPOSITION AND POTENTIALLY
ALZHEIMER'S DISEASE DEMENTIA AS
WELL.
NEXT WE WANTED TO ADDRESS
HEARING AND THE HEARING LOSS IS
AMONG THE RISK FACTORS FOR
DEMENTIA.
NEXT SLIDE AND I ENDED UP
MEETING FRANK LYNN AT A SEMINAR
SHOWING HEARING LOSS IS PRESENT
IN THE MAJORITY OF OLDER ADULTS.
BY THE TIME YOU GET TO 70 IT'S
50% BY THE TIME YOU GET TO 90%
IT'S ALMOST 100%.
AND BASICALLY WE DID A CLINIC
TRIAL GOING FROM PILOT TO BEING
FUNDED TO RANDOMIZE 970 PEOPLE
RIGHT BEFORE COVID AND WE JUST
REPORTED THE ANSWER IS IN LANCET
THIS SUMMER.
INTERESTING RESULTS.
BASICALLY THE PRIMARY OUTCOME
SHOWED NO PROTECTION.
ON THE OTHER HAND IF YOU
STRATIFY BY PARTICIPANTS WE
SHOWED NO REDUCTION IN
SELF-SELECTED VOLUNTEERS.
ON THE OTHER HAND ON THE ARIC
COHORT YOU CAN SEE IF SOMEBODY
WAS IN THE COHORT AND WE
APPROACHED THEM AND SAID YOU
HAVE A HEARING LOSS, DO YOU WANT
A HEARING AID DEVICE?
WE GOT A 48% REDUCTION IN
COGNITIVE DECLINE.
THIS IS A WHOLE CHALLENGE.
CLINICAL TRIALS ARE THE BEST BUT
IF YOU RELY ON VOLUNTEERS, WHICH
YOU MUST, VOLUNTEERS WILL RUN TO
YOU SAYING I'M AFRAID OF GETTING
DEMENTIA OFTEN DON'T GET
DEMENTIA.
IT'S A REAL ISSUE.
THIS IS ONLY OVER THREE YEARS.
WE'VE BEEN FUNDED TO DO EXTENDED
FOLLOW AND DEMENTIA IS A 50 YEAR
PROCESS BUT WE CAN'T WAIT SO
THIS IS COGNITIVE DECLINE AND
NEED TO UNDERSTAND SURROGATE
OUTCOMES AND PROTEOMES AND OTHER
PEOPLE SHOWING BRAIN IMAGES ARE
PROMISING.
OVER ALL I WANTED TO REMIND US
THAT THE AMERICAN HEART
ASSOCIATION'S LIFE ESSENTIAL
EIGHT IF YOU CLICK REALLY TELLS
US THAT IF YOU ANIMATE THE CLICK
THAT WE KNOW WHAT TO DO.
WE WANT TO SAY -- CAN YOU
ANIMATE THE CLICK?
WE KNOW WHAT TO DO.
I THINK ALL OF US WANT TO SAY,
OH, WELL, WHAT WILL MAKE US
HEALTHY?
THE TRUTH IS EATING HEALTHY,
FITNESS, NOT SMOKING IS HUGE AND
SLEEPING WELL.
HOW WE ACHIEVE THAT IS HARD.
SO HOW WE GET THIS DONE,
BEHAVIOR CHANGE, PEOPLE I THINK
ARE THE FINAL FRONTIER OF A LOT
OF DISEASE BECAUSE A LOT OF
CHRONIC DISEASE IS BEHAVIORALLY
ORIENTED.
THE BIG DATA PROMISE IS HUGELY
PROMISING FOR UNDERSTANDING
MECHANISMS AND FOR ME BIO
MEDICAL INTERVENTION.
SO ONCE I GET FAR ENOUGH ALONG
AND I GET A HEART ATTACK THEN I
NEED DIFFERENT TREATMENT.
IF I HAVE CANCER, TELLING ME TO
EAT WELL IS NOT GOING TO BE
ENOUGH AND WE NEED TO KNOW WHAT
CANCER, WHAT THERAPY.
THE SAME THING WITH OTHER
INFECTIONS AND OTHER CAUSE.
LAST SLIDE, I WANTED TO THANK
ALL THE PEOPLE INVOLVED IN THIS
AND BASICALLY I THINK THERE'S A
CLICK ANIMATION TOO.
THE PARTICIPANTS AND
COLLABORATORS AND STAFF.
THIS A TEAM SCIENCE ON THE LEFT
IS OUR DATA COLLECTION TEAM ON
HALLOWEEN AND YOU KNOW THEY'RE
GOOD WHEN I SHOWED UP AND GOT A
COSTUME AND IN THE MIDDLE ARE
RESEARCHERS INCLUDING MANY
JUNIOR INVESTIGATORS PRODUCING
SOME OF THE BEST SCIENCE IN THE
CARDIOLOGY CONSORTIUM.
>> THAT WAS WONDERFUL.
WE HAVE A COUPLE QUESTIONS
BEFORE WE DIVE IN WE'RE GOING TO
SHARE THE RESULTS FROM THE FIRST
POLL.
>> I THINK IT'S A WONDERFULLY
INSIGHTFUL PICK UP ON AN ISSUE
THAT OBSESSES ME.
PEOPLE IN THIS COUNTRY ARE
EXPERIENCING FATIGUE.
WE HAVE TOO MANY PEOPLE AFTER
US.
WE CAN NO LONGER GET 95% OF
PEOPLE TO COME TO CLINIC.
WHAT WE HAVE TO DO IS PROVIDE
VALUE TO PEOPLE AND USE MULTIPLE
LAYERS OF FOLLOW-UP.
I THINK WE DO IT IN ARIC.
I'M SURE ALL OF US DO IT AS WELL
AS AND MILLION VETERANS IS PART
OF THE SYSTEM.
IF WE CAN COMBINE PASSIVELY
SYMPTOMS THAT CAPTURE EVERYBODY
LIKE THE DEATH INDEX YOU CAN'T
DIE WITHOUT THE U.S. KNOWING AND
WE CALL PEOPLE AND ACCEPT THE
FACT THAT CLINIC VISITS ARE
ACTUALLY HARDER TO GET.
I JUST ANSWERED THE POLL.
I GUESS I'M SENIOR FACULTY.
EVERYBODY ELSE ANSWER?
THERE'S A QUESTION WHETHER THE
AGE OF THE PEOPLE STRATIFIED BY
DISEASE ONSET OR AND WHETHER
IT'S LIFE TIME FOR TIME FOR
INCIDENT.
>> THIS IS STRATIFIED AT AGE AT
BASELINE.
>> THIS WAS STRATIFIED AT THE
AGE OF BASELINE AND WITHIN THE
BASELINE WE HAVE A 20-YEAR SPAN
IN ARIC TO DIVIDE PEOPLE.
IT'S FORESHADOWING OF OUR WORK.
NOW WE HAVE 30 YEARS OF
FOLLOW-UP.
USING SENSORING WE CAN DO LIFE
TIME RISK AND WE'RE WORKING ON
THAT AND I THINK IT'S
FASCINATING HOW COMPLICATED
THINGS ARE.
FOR EXAMPLE, FOR DIABETES WE
TRIED TO PUBLIC THERE'S RISK
WITH PRE-DIABETES AND MOST THE
RISK IS MEDIATED FROM MOVING
FROM PRE-DIABETES TO DIABETES
AND IT'S IMPORTANT AND IF YOU
CAN DELAY DIABETES, EARLY ONSET
DIABETES IS A HUGE RISK AND
LATER ONSET DIABETES IS A LOWER
RISK.
WITH DEMENTIA MOST OF THAT IS
LAY.
NOT MANY CASES FOR US TO LOOK
AT.
THANK YOU.
>> WITH HAVE ONE MORE POLL
LAUNCHED SO THAT WILL HELP US
AND THANK YOU CORESH FOR THE
FANTASTIC TALK.
AND WE WILL MOVE ON TO THE LAST
SPEAKER OF THIS SESSION, WHO IS
DR. PSATY AND HE WILL BE TALKING
ABOUT THE ORIGINS AND STRUCTURE
OF THE COHORTS FOR HEART AND
AGING RESEARCH IN GENOMIC
EPIDEMIOLOGY OR THE CHARGE
CONSORTIUM.
DR. PSATY IS A PROFESSOR AT THE
UNIVERSITY OF WASHINGTON AND
EXPERT IN MEDICINE, EPIDEMIOLOGY
AND HEALTH SYSTEMS.
AND ALSO CO-DIRECT THE
CARDIOVASCULAR HEALTH RESEARCH
UNIT.
HE EARNED HIS Ph.D. FROM INDIANA
AND M Ph.D FROM UW AND
EPIDEMIOLOGY HE'S CONTRIBUTED TO
MULTIPLE NIH FUNDED STUDIES
INCLUDING THE ADVISORY COMMITTEE
HEALTH STUDY AND CHAMPIONS
INNOVATIVE DATA METHODS AND MULT
STUDY COLLABORATIONS AND FOUNDED
THE CHARGE CONSORTIUM TO ADVANCE
RESEARCH IN GENOMIC
EPIDEMIOLOGY.
WITH THAT, PLEASE TAKE IT AWAY.
>> THANKS FOR THE INTRODUCTION
AND UP INVITATION.
OTHER STUDIES WERE INVOLVED
INCLUDING ARIC AND FRAMING HACK
AND RODDERDAM AND OTHERS AND
I'LL FOCUS ON THE STRUCTURE OF
THE CHARGE CONSORTIUM.
THIS TALK WILL BE DEVOID OF
GENETIC DATA.
YOU WON'T SEE A SINGLE ODDS
RATIO IN THE STUDY.
I WANT TO DEFINE A STUDY AS
PARTICIPANTS AND INVESTIGATORS
AND JOE'S LAST SLIDE WAS A GOOD
ILLUSTRATION.
THERE WERE FIELD CENTER STAFF,
THERE ARE COLLABORATORS LOCALLY
AND COLLABORATORS
INTERNATIONALLY.
A STUDY REALLY INCLUDES ALL OF
THOSE TOGETHER.
A LOT OF THE INTEREST HAS BEEN
IN AN IN THIS CONFERENCE IN DATA
SHARING AND THE NIH HAS DATA
SHARING METHODS THAT PROVIDE
DATA OPENLY TO PEOPLE.
THE BIO LINK AND GB GAP PEOPLE
CAN GO TO THE DATA AND GRAZE.
WE SUPPORT THAT AND PUBLICLY
FUNDED DATA OUGHT TO BE PUBLICLY
AVAILABLE AND CONTRAST THAT WITH
CHS AND CHARGE AND THAT'S ACCESS
AND WHAT I'LL DESCRIBE TODAY IN
PART AND THE GOALS AREN'T JUST
OF MAKING DATA AVAILABLE IT'S
PRODUCING HIGH-QUALITY WORK.
IT'S HIGH LEVELS OF PRODUCTIVITY
AND TRAINING OF EARLY CAREER
INVESTIGATORS.
CHS IS LIKE FRAMINGHAM.
IT'S A COHORT STUDY.
WE HAVE 5,588 OLDER ADULTS FOR
STROKE AND CARDIOVASCULAR
DISEASE AND THERE WERE SUB
CLINICAL DISEASE MEASURES AND
ADVANCED FOLLOW-UP.
THIS IS A BRIEF SUMMARY OF THE
MEASURES.
CAROTID ULTRASOUND, ECHO
CRANIOLOGY AND MRI AND RETINAL
PHOTOGRAPHY.
THIS LOOKS LIKE A LOT LIKE THE
EXAMINE SCHEDULE THAT WE SAW IN
ARIC.
ADDITIONALLY, EVENTS HAVE BEEN
COLLECTED AND THERE'S BEEN MANY
MANY ANCILLARY STUDIES THAT
INCLUDED COGNITIVE FUNCTION,
OMICS DATA, GENETICS AND FORTH.
SO THE NEW MODEL OF DATA SHARING
WAS REALLY DEVELOPED IN THE
EARLY 2000s AND FORMED THE REAL
WORKING GROUP IN 2001.
THEY RECRUITED NEW INVESTIGATORS
FROM NON-CHS SITES PROVIDED
INFORMATION ABOUT HOW TO ACCESS
THE DATA, PROVIDED ANALYTIC
SUPPORT AND THE RESULT HERE WAS
A WORKING GROUP THAT PRODUCED
MULTIPLE GRANTS AND PAPERS AND
MANY OF THESE FEATURING WORK AND
CHS WAS REALLY A MODEL IN
ACTIVE DATA SHARING AND FOLLOWED
THAT UP IN 2005 AND 2007 WITH
WORKSHOPS.
JEANIE OLSON AND DAVID TOOK THE
LEAD HERE AND THEY IDENTIFIED
PEOPLE THAT WERE K AWARDEES AND
IT SPAWNED A HALF DOZEN WORKING
GROUPS AND STEWARDS OF THE DATA.
THE BLUE HAVE THE INVESTIGATORS
FUNDED ON THE GRANT OR CONTRACT
YOU AND GREEN ARE COLLABORATORS
AND SEE THE FIRST AUTHORS
INCREASED MARKEDLY AND AFTER
2005 AND 2007 THERE WERE FURTHER
INCREASES.
THE ADVANTAGE IS THESE YOUNG
INVESTIGATORS CAN REALLY BE
CHAMPIONS.
SO WITH GUIDANCE AND STATISTICAL
SUPPORT THEY CAN MOVE PAPERS AND
BE HIGHLY PRODUCTIVE.
IS LIKE FRAMINGHAM AND ARIC AND
OTHERS, CHS HAD GWAS ANCILLARY
STUDY AND HAD GENOME-WIDE DATA
IN ABOUT 4,000 PARTICIPANTS.
THE PRIOR AIM WAS CBD EVENTS.
THERE WERE SECONDARY AIMS.
THE DATA WERE ALL SHARED
PUBLICLY WITH GB GAP AS
REQUIRED.
AS WE BEGAN TO DO THE ANALYSIS
WE FOUND LOW POWER FOR THE
EMERGING EFFECT SIZES.
THAT WAS THE BEGINNING OF
CHARGE.
CHARGE WAS A SIDE EFFECT OF THE
DISRUPTIVE GOOD WAS TECHNOLOGY.
IT WAS A MATTER OF A SEARCH FOR
IMPROVED POWER OR REPLICATION
AND MANY PEOPLE CONTRIBUTED.
CHRIS O'DONNELL AND ERIC
BOERWINKLE WE CAME TOGETHER AND
THE COHORT DESIGN IS THE
ORGANIZING PRINCIPAL.
THAT MEANS ALL THE PHENOTYPES
YOU SAW EARLIER IN MY SLIDES AND
THE OTHER PHENOTYPES YOU SAW IN
JOE'S SLIDES WERE AVAILABLE FOR
WHOLE GENOME ANALYSIS.
AND OVER TIME WE ADDED COHORTS,
MESA HEALTH, ABC, JACKSON, THE
HISPANIC COMMUNITY HEALTH STUDY
HAS BEEN INCLUDED.
AND CHARGE IS A VOLUNTARY
FEDERATION.
THE SOURCE OF THE SIEBS IS
BOTTOM UP.
AND
-- THE DESIGN IS BOTTOM UP
ACROSS MULTIPLE PHENOTYPES.
SO CHARGE IS REALLY AN EXTENSION
OF THE NIH APPROACH TO THE
MULTI-CENTER STUDIES.
INSTEAD OF A MULTI-SITE STUDY WE
HAVE A MULTI STUDY CONSORTIUM.
THE KEY ELEMENT HAS BEEN A
COORDINATING CENTER.
IT'S SUPPORTED BY AN
INFRASTRUCTURE GRANT.
THERE'S TEN SUBCONTRACTS TO THE
PARTICIPATING COHORT.
A LARGE AMOUNT OF THE GRANT GOES
TO THE COHORT.
WE ARE NOT ASKING PEOPLE TO
VOLUNTEER AND HELP RUN THE
GRANT.
THEY RECEIVE FUNDING TO
PARTICIPATE AND WE HAVE HAD TWO
MEETINGS A YEAR IMPORTANT FOR
COHESION AND TRAVEL AWARDS FOR
INVESTIGATORS.
THE ORGANIZATION IS A MATRIX,
THREE DIMENSIONAL.
THE STEERING COMMITTEE
REPRESENTS EACH OF THE COHORT
AND THERE'S MEMBERS FROM THE
STEERING COMMITTEE.
THERE'S 40 ACTIVE WORKING GROUPS
RIGHT NOW AND THEY INCLUDE CROSS
COHORT PHENOTYPE TEAMS.
OVER TIME SOME OF THESE HAVE
COME AND SOME HAVE GONE.
NEW ONES HAVE FORMED UP AND
THERE'S OTHER COMMITTEES LIKE
ANALYSIS, GENOTYPING, PROTEOMIC
AND MENDELIAN RANDOMIZATION THAT
WORK ACROSS THE COHORTS AND
WORKING GROUPS.
THIS WEBSITE INCLUDES THE CHARGE
PRINCIPLES AND NO SURPRISES.
THE WORKING GROUP MODELS WITH
MEMBERS, PRIORITIES, PHENOTYPES,
COLLABORATIONS, THEY DEVELOP
GRANT APPLICATIONS.
THEY CAN INCLUDE MEMBERS FROM
ANY OTHER STUDY.
THEY DON'T HAVE TO STAY
EXCLUSIVELY WITHIN THE CHARGE
COHORTS.
THERE ARE EUROPEAN STUDIES,
ASIAN STUDIES AND MANY OTHERS
THAT PARTICIPATE IN THE WORKING
GROUPS.
THE WORKING GROUPS TRY TO
IDENTIFY CHAMPIONS.
AGAIN, I THINK THE CHAMPION IS A
KEY FEATURE HERE.
AND THE MANUSCRIPT IS THE UNIT
OF WORK.
WE LIKE PROSPECTIVE PROPOSALS
AND ANALYSIS PLANS.
IT ESTABLISHES RESPONSIBILITIES
AND AUTHOR LISTS AND INDIVIDUAL
GROUPS CAN OPT IN OR OUT.
THE EARLY CHARGE ANALYSIS
METHODS WERE PRESPECIFIED
ANALYSIS PLAN CONDUCTED WITHIN
STUDY BY LOCAL EXPERTS AND WE
DID BETWEEN COHORT META-ANALYSIS
OF THE RESULTS.
THAT MEANT WE COULD SHARE
RESULTS ON AN INDIVIDUAL LEVEL
DATA.
THIS WAS EFFICIENT AND AVOIDED
IRB AND DATA MATERIAL ISSUES.
IT'S ACTUALLY MORE POWERFUL THAT
BE THE TWO-STAGE DESIGN WITH
DISCOVERY AND REPLICATION OF
SUBSET AND IMPORTANTLY IT AVOIDS
THE CLASS DISTINCTION BETWEEN
DISCOVERY AND REPLICATION.
THE META-ANALYSIS METHOD THANKS
TO OUR BIO STATISTICIANS HAS
BEEN CULTURALLY POWERFUL FOR
COLLABORATION.
SO IN THE OLD DAYS WHEN CHS AND
OTHERS WERE HUMMING ALONG WE HAD
SINGLE COHORT PAPERS.
THERE WERE FIVE TO 10 AUTHORS
WITH 5,000 OR 15,000 SUBJECTS
AND THERE WAS POLITE COMPETITION
IN THE COHORT STUDIES.
NOWADAYS YOU CAN ACQUIRE DATA
FROM GB GAP AND BENEFIT FROM THE
LOCAL KNOWLEDGE AND THE
COLLABORATION MODEL HAS WORKED
WELL.
THE LARGE-SCALE GWAS PAPERS
OFTEN INCLUDE 50 TO 300 AUTHORS
WITH 100,000 TO 2.5 MILLION
PARTICIPANTS.
IT'S REALLY QUITE EFFICIENT WITH
A GOOD PARTICIPANT RATIO.
SOO THE CHARGE MEETINGS HAVE
BEEN CRITICAL.
WE STARTED PHONE CALLS FOR
CHARGE IN 2007.
HAD A SERIES OF MEETINGS OVER
THE YEARS.
A COUPLE WERE VIRTUAL DUE TO THE
PANDEMIC BUT WE'RE NOW BACK INTO
UP PERSON MEETINGS WITH THE ONE
IN MAY WILL BE IN DENVER AND A
YEAR FROM NOW A CONDITIONAL
MEETING IN RODDERDAM.
THE CHARGE PRODUCTS ONE OF THE
MOST IMPORTANT ONES IS A NOVEL
RESEARCH STRUCTURE WITH
INSTITUTIONAL TEAMS AND TRAINING
EFFORTS.
CHARGE HAS CONTRIBUTED TO
PARTICIPATED IN A CULTURAL
TRANSFORMATION THAT RESULTED IN
HIGH LEVEL COLLABORATIONS IN THE
STUDIES.
ONE OF THE SIDE EFFECTS HAS BEEN
A NUMBER OF NON-GENETIC
COLLABORATIONS AMONG THESE
COHORTS.
CHARGE HAS MORE THAN 1200
PUBLICATIONS AND AGE INDEX AT
ABOUT 163.
THERE ARE 40 WORKING GROUPS THAT
REMAIN LARGELY ACTIVE.
SOME HAVE MERGED WITH PUB MED.
SOME WORK WITH M.V.P. AND THEY
HAVE 21 ACTIVE GRANTS.
WE ALSO HAVE A CHARGE GB GAP
WITH A SHARING SITE.
SO SOME OF THE DIRECTIONS HAVE
BEEN LARGER SAMPLE SIZES
EMERGING COHORT OF CONSORTIA.
MULTIETHNIC ANALYSES, BIO
INFORMATICS WORK.
SOME WORKING GROUPS INCLUDE
BASIC SCIENTISTS THAT CONDUCT
FUNCTIONAL STUDIES.
NEW PROTEOMIC AND METABOLOMIC
DATA HAVE HELPED TO REINVIGORATE
A NUMBER OF THE WORKING GROUPS.
THE PUBLICATIONS ARE NOW DENSER
AND MORE COMPLEX THAN THEY WERE
IN THE EARLY 2000s.
THERE'S MANY COLLABORATIONS WITH
PUB MED AND THE AABVAILABILITY
THE WHOLE GENOME SEQUENCE.
IT'S NOT REALLY POSSIBLE TO DO
LOCAL ANALYSIS AND SHARE THE
RESUL
RESULTS QUE NEEDED A METHOD FOR
POOLING DATA AND IF FRAMINGHAM
WERE TO SHARE DATA SO EACH GROUP
COULD HAVE IT IT WOULD BE
TRANSFERS THAT ARE JUST NOT
FEASIBLE.
ONCE THE BECAME AVAILABLE CHS
HELPED CREATE A SINGLE
CONSORTIUM DOCUMENT THAT STUDIES
CAN SIGN ON TO AND THEN SHARE
DATA.
THIS SOUNDS LIKE AN EASY THING.
WELL, IT WASN'T.
AND THERE'S MULTIPLE DIFFERENT
UNIVERSITIES BUT IT NOW WORKS.
WE HAVE A DATA SHARING AGREEMENT
IN CHARGE AND WE SHARED THIS
WITH OTHERS.
THE COVID CONSORTIUM ADOPTED
THIS METHOD AND WITHIN THREE
MONTHS THEY HAD MOST OF THEIR
STUDIES SIGNED ON ABLE TO SHARE
DATA ACROSS ALL SITES.
SO THE ANALYSIS COMMONS IS THE
RESULT OF THIS AND THIS IS
DESCRIBED IN NATURE AND GENETICS
AND ONE IS SIGNED BY EACH STUDY
IN THE COLLABORATION.
THE PRIMARY PURPOSE IS POOLING
FROM THE WHOLE GENOME SEQUENCE
DATA.
THERE'S FEWER ANALYSIS AND
GREATERESE OF USE.
-- GREATER EASE OF USE.
>> TWO-MINUTE WARNING.
>> OKAY.
THERE'S MORE POWER AND IT
REQUIRES MORE CENTRAL
COORDINATION.
IT WORK IN CHARGE BUT IT WOULD
NOT AND DOES NOT SCALE WELL.
SO I THINK THE NIH HAS BEEN
ALMOST DISRESPECTFUL OF THE
INVESTIGATORS' EFFORTS.
WE HARMONIZED DATA MULTIPLE
TIMES AND THERE'S NOT A
MECHANISM FOR EASILY SHARING THE
HARMONIZED DATA.
THEY NEED TO GO BACK TO THE
STUDIES AND IT WOULD BE IDEAL TO
HAVE A METHOD FOR HARMONIZING
DATA AND MAKING THAT HARMONIZED
DATA READILY AVAILABLE.
AND TO MY KNOWLEDGE THIS DOESN'T
EXIST.
I THINK WHAT WE DO IS POPULATION
BIOLOGY.
WE DO GENETIC ASSOCIATIONS ON
LARGE SAMPLES FOR DISCOVERY OF
NEW BIOLOGY IN HUMANS WITH THE
NEW OMICS TOOLS AND POWERFUL TO
IDENTIFY TOPICS.
I THINK KEENAN WALKER'S PAPER IS
A NICE EXAMPLE.
THERE'S PRECISION MEDICINE
OPPORTUNITIES BUT I THINK IT'S
IMPORTANT YOU HAVE EVIDENCE OF A
BENEFIT RATIO.
CHARGE HAS CONTRIBUTED TO DATA
SHARING METHODS, TRAINING YOUNG
INVESTIGATORS AND I THINK THOSE
ARE IMPORTANT ACHIEVEMENTS.
IN MY MORE CURMUDGEONLY MOVES
IT SOMETIMES SEEMS THERE'S TWO
MANY DATA CHASING TOO FEW GOOD
QUESTIONS.
I WOULD ENCOURAGE PEOPLE TO
THINK ABOUT THE QUESTION.
GOOD SCIENCE INVOLVES GOOD
QUESTIONS AND HIGH QUALITY DATA
AND STRONG METHODS.
WE NEED THAT COMBINATION AND NOT
JUST LET'S JUST PREDICT THIS.
THIS IS THE RESEARCH STEERING
COMMITTEE INCLUDING EMERITUS
MEMBERS.
AND THESE PEOPLE HAVE ALL
CONTRIBUTED TO CHARGE AND ITS
VISION AND ACTIVITIES.
THANK YOU.
>> FANTASTIC.
THANK YOU SO MUCH DR. PSATY.
THERE'S ONE QUESTION ABOUT
IDENTIFYING THE COHORTS THAT
BECOME INVOLVED WITH THE CHARGE.
ISN'T THAT THE CHARGE STEERING
COMMITTEE IDENTIFIES AND REACHES
OUT TO EXISTING COHORTS OR IS IT
THAT EXISTING COHORTS GLOBALLY
REACH OUT TO CHARGE?
HOW ARE THE CONNECTIONS MADE?
SO RIGHT NOW THERE IS A GRANT
AND THAT GRANT INCLUDES ABOUT 10
COHORTS AND THEY RECEIVE FUNDING
AND THEY'RE REPRESENTATIVED ON
THE STEERING COMMITTEE BUT OTHER
COHORTS ARE WELCOMED TO JOIN.
WE DON'T HAVE FUNDING FOR THEM
AND IN FACT IN THE SAN ANTONIO
MEETING THERE'S A SESSION WHERE
SIX NEW COHORTS ARE PRESENTING
AND HOPING TO WORK WITH CHARGE
ONE WAY OR ANOTHER.
THE WAY TO LINK UP WITH CHARGE
IS THROUGH A WORKING GROUP WHERE
IF THERE'S A PHENOTYPE THAT THAT
COHORT REALLY HAS INTEREST IN OR
TO WE CAN LINK THEM UP WITH THE
WORKING GROUP AND THEY CAN
PARTICIPATE IN CHARGE.
>> EXCELLENT, THANKS.
AND ONE ADDITIONAL QUESTION IS
THE ANALYSIS COMMONS MODEL
DOESN'T SCALE.
>> IT'S BECAUSE THE GROUP IS
TREATED AS A STUDY.
AND IT IS NOT FOR OPEN DATA
SHARING ACROSS ALL PEOPLE.
BECAUSE A CENTRAL GROUP HAS TO
MANAGE WHO GETS ACCESS TO WHICH
DATA FOR WHICH ANALYSES.
DOES THAT MAKE SENSE?
>> GREAT.
THANK YOU.
WE'LL NOW MOVE TO Q&A SESSION
INCLUDE ALL THE SPEAKERS FROM
THE PREVIOUS SECTION.
WHILE WE MOVE TO THE Q&A, WE
HAVE THE RESULTS OF THE TWO POLL
QUESTIONS THAT ARE RELEASED.
HOPEFULLY EVERYBODY CAN SEE THE
RESULTS.
THE FIRST IS THE PRIMARY
OBJECTIVE FOR ATTENDING THE
WORKSHOP.
WE SEE A VAST MAJORITY OF PEOPLE
ARE LOOKING TOWARDS FUTURE
TRENDS AND MANY ARE LOOKING
TOWARDS NEW TOOLS AND
TECHNIQUES.
IF THERE ARE PEOPLE WHO WERE
LOOKING TO LEARN NEW TOOLS AND
TECHNIQUES THAT MAY HAVE MISSED
SOME OF THE TUTORIALS, THOSE
HAVE MATERIALS AVAILABLE SO
CHECK THAT OUT AND WE HAD
ANOTHER QUESTION ABOUT WHO WAS
IN ATTENDANCE AND GREAT TO SEE
WE HAVE A DIVERSE SET OF
ATTENDEES FROM ACROSS DIFFERENT
STAGES RANGING FROM EARLY CAREER
ALL THE WAY TO SENIOR FACULTY.
WELCOME EVERYONE AND PLEASE DO
ADD ANY QUESTIONS THAT YOU HAVE
FOR OUR SPEAKERS INTO THE Q&A
BOX.
>> AS WE ASSEMBLE THE QUESTIONS
I WANT TO REMIND PEOPLE WE'LL
GET A BREAK FROM 12:30 TO 1:00
AND THINK THE LAST SESSION
WESTBOUND -- WILL BE EXCITING
AND INTERESTING STARTING WITH
ERIC BOERWINKLE EXTREMELY WISE
AND KNOWLEDGEABLE.
BRUCE, AMAZING SUCCESS AT
CHARGE.
EMPHASIZED THE IDEA OF
COMMUNITY.
CAN WE MEASURE COMMUNITIES AND
MAYBE TALKING TO DR. PYARAJAN IF
I PRONOUNCED IT CLOSELY, HOW
DOES A STUDY AS BIG AS ONE
MILLION VETERANS FOSTER
COMMUNITY AMONG INVESTIGATORS
AND MENTORSHIP AND PEOPLE USING
THE DATA BECAUSE I THINK
EVENTUALLY IT'S OUR PATIENTS BUT
ACTUALLY BY PEOPLE SO THE
ANONYMOUS MODEL I TEND TO VIEW
HIS SENTIMENT IT NEEDS TO BE
FREE BUT ANONYMIZED DOESN'T WORK
FOR SCIENTIFIC RESEARCH.
>> MY ANSWER ARE THE MEETINGS.
THEY'RE EXCITING.
PEOPLE CAN TALK ON THE SIDE.
THEY CAN FORM COLLABORATIONS.
WE HAVE WORKSHOPS THAT GO ALONG
WITH THE MEN -- MANDELIAN
RANDOMIZATIONS.
IF IT WAS JUST CALLS I DON'T
THINK IT WOULD HAVE LASTED.
>> I WAS THINKING ABOUT THIS
WORKSHOP FOR SOME PARTICIPANTS
TO FULLY BENEFIT IT'S GREAT TO
GET CAUGHT UP.
QUESTIONS FOR THE V.A.?
>> WE HAVE -- ONE OF THE THINGS
WE HAVE BEEN A PERSON THAT'S
ENDED UP AT THE V.A. TOO WE
TRIED TO PROMOTE AND ONE THING
WE'RE BEING CRITICAL AND THIS IS
SORT OF CRITICISM FOR THE V.A.
NOT RELEASING THE DATA
COMPLETELY OPEN AND AVAILABLE
EVERYWHERE AND THE UNSTRUCTURED
DATA WE WANT TO ENSURE WE
RELEASE IT IN A RESPONSIBLE WAY
THAT KEEPS THE DATA SECURE AND
THE PARTICIP PARTICIPANTS THE V
SECURE AND WE HAVE EXAMPLES AND
WE HAVE BEEN ENCOURAGING
RESEARCHERS TO GO AND
PARTICIPATE AND PRESENT AND TALK
TO FOLKS.
THE OTHER THING WE HAVE BEEN
DOING FOR THE NEW PROJECTS
COMING THROUGH WE'RE ENCOURAGE
CONSORTIUMS TO BE FORMED.
WE HAVE STUDIES WITH MORE THAN
40, 50 RESEARCH GROUPS MORE THAN
40, 50 FOLKS COLLABORATING FROM
ACROSS THE COUNTRY AND AS I SAID
EARLIER ACADEMIC RESEARCHERS
PARTICIPATING WITH THEIR
COLLEAGUES WITH A V.A.
APPOINTMENT TO WRITE GRANTS AND
REALLY PROMOTING THAT.
>> THANK YOU.
>> AND ALONG THOSE LINES, ALL OF
US ARE AWARE THAT THE RISK OF
RE-IDENTIFICATION OF STUDY
PARTICIPANTS IS NEVER ZERO.
SO WE HAVE THIS TRADE-OFF THAT
WE HAVE TO THINK ABOUT OF THE
BALANCE BETWEEN THE RISKS AND
BENEFITS TO STUDY PARTICIPANTS.
AND I'M CURIOUS FOR ALL OF THE
SPEAKERS OF THIS SESSION MAYBE
IT START WITH DR. DENNY ON THE
QUESTION OF HOW DO YOU SOMEONE
INDICATE THE TRADE-OFF OF RISK
AND BENEFIT TO PARTICIPANTS AND
THE POTENTIAL FOR
RE-IDENTIFICATION BALANCED WITH
THE POTENTIAL FOR DOING GOOD?
>> IT'S A GREAT QUESTION.
I CAN GO BACK TO THE WORK AT
VANDERBILT AND BUILDING HIPAA
DE-IDENTIFIED REPOSITORY LINKED
TO DNA.
THE DIFFERENCE WITH ALL OF US IS
IT STARTED WITH 17 CITIES AND
ASKED ABOUT THE SCENARIO WITH
THE POPULATION AND BUILT THE
PROGRAM UP FROM WHAT AT THAT
TIME WOULD HAVE BEEN POTENTIAL
PARTICIPANTS BECAUSE WE DIDN'T
HAVE A PROGRAM.
OFF WHAT THEY TOLD US WAS
IMPORTANT.
SO WE TRY TO MAKE IT CLEAR IN
THE CONSENT THAT WE'RE NOT GOING
TO BE SHARING YOUR DIRECT
IDENTIFIERS AND THINGS LIKE THAT
BUT IT DOESN'T MEAN IT'S
IMPOSSIBLE FOR YOU TO
REIDENTIFY.
WE'LL DO THE BEST WE CAN AND
HAVE VIDEOS ON CONSENT AND
PRIVACY AND THINGS LIKE THIS
SECURITY.
THAT'S ONE OF THE RISKS OF THE
PROGRAM.
IN ADDITION I'M PRETTY SURE IT'S
LIKE THE QUESTIONS THAT WE ASK
THE PARTICIPANT TO MAKE SURE
THEY UNDERSTAND CORRECTLY WHAT
THE PARTICIPANTS SAY AND AT THE
SAME TIME PEOPLE WANT US TO
DISCOVER STUFF.
THEY WANT US TO MAKE A
DIFFERENCE.
THAT'S WHY THEY'RE JOINING AND I
THINK A LOT OF TIMES WE MIGHT BE
MORE WORRIED ABOUT THAT THAN
SOME OF OUR PARTICIPANTS ARE.
LOTS OF PEOPLE ARE GIVING
INFORMATION TO CREDIT CARD AND
NETFLIX AND OTHER COMPANIES OUT
THERE THAT IS SOLD TO OTHER
PEOPLE VERSUS WHAT THEY GIVE TO
US.
SO IT'S IMPORTANT.
>> WHAT'S PRIVATE AND WHAT
CANNON SHARED WITH THE ADVENT OF
SOCIAL MEDIA SO TO SAY.
BACK IN 2009, 2008 WHEN WE WERE
DOING A LITTLE SURVEY THE
VETERANS WERE VERY ALTRUISTIC IN
SAYING TAY HAVE NO HESITATION TO
SHARE DATA WITH ACADEMIC
INSTITUTIONS.
THEY WERE NOT KEEN ON SHARING
FOR PROFIT AND THAT'S EVOLVED
OVER THE TIME IN TERMS OF WHAT
THEY ARE WILLING TO SHARE AS
PART OF THE LARGE BIO BANK.
IN GENERAL I AGREE THE V.A. HAS
TAKEN THE IDEA TO ERROR ON THE
SIDE OF CAUTION AND WE HAVE BEEN
FIGURING OUT THE NUANCES OF AS A
QUESTION IT DEPENDS ON THE
NUANCES IN TERMS OF WHAT CAN BE
REIDENTIFIED AND WHAT CAN BE
TRULY ANONYMIZED.
>> THAT'S GREAT.
LOOKS LIKE WE HAVE A QUESTION
FROM DR. BOERWINKLE IF YOU WANT
TO JUMP IN.
>> THERE'S A LOT OF WISDOM IN
THIS SESSION BUT I'LL PICK ON
YOU, BRUCE, FOR FUN.
YOU BROUGHT UP THE IMPORTANCE OF
ASKING A GOOD QUESTION.
HOW DO WE MENTOR -- FIRST
OURSELVES AND HOW DO WE TALK TO
THE FIELD ABOUT THE IMPORTANCE
OF A GOOD QUESTION BUT KEEPING
IN MIND THAT THE WORLD RIGHT NOW
IS GA-GA ABOUT THE POSSIBLE OF
HYPOTHESIS-FREE, ARTIFICIAL
INTELLIGENCE ETCETERA.
THERE'S A GROWING SENTIMENT WHAT
YOU REALLY NEED IS NOT TO BE
VERY SMART BUT RATHER TO HAVE A
REALLY BIG AND CLIMB A LADDER
AND DUMP THE DATA AT THE TOP AND
PUSH THE ON BUTTON AND THE NEXT
DAY INSIGHT WILL COME OUT THE
BOTTOM.
HOW DO WE BALANCE THOSE IN 2023?
WELL, THANKS, ERIC FOR THAT
QUESTION.
IN THE OLD DAYS WHEN WE HAD TO
WRITE GRANTS AND COLLECT THE
DATA AND GET FUNDING FOR THE
DATA COLLECTION TO ANSWER A
QUESTION, THE QUESTION USED TO
BE CRITICALLY IMPORTANT.
AND SO I THINK THE METHOD THERE
WAS TO LOOK AT THE LITERATURE,
KNOW THE LITERATURE WELL AND ASK
WHAT THE NEXT ANSWERABLE
QUESTION MIGHT BE.
THINGS HAVE SHIFTED A BIT WITH
THE BIG DATA BUT I THINK FOR BIG
DATA ANALYSIS I TEND TO PREFER
ETIOLOGIC QUESTIONS.
WHAT WHERE THE CAUSES OF DISEASE
AND HOW CAN WE USE OMICS TO
VERIFY THE VARIANTS AND ARE THEY
TARGETABLE AND DOES MENDEL IIAN
SYSTEMS HELP AND JOE MADE A
POINT IN EMPHASIZING WE NEED TO
THINK WITH THE POSITIVE
PREDICTIVE VALUE AND THE
INCREASE OF .02 IS REALLY
NOTHING TO WRITE HOME ABOUT.
AND PREDICTION IS IMPORTANT FOR
INSTANCE IF A THERAPY THAT MIGHT
BE OFFERED IS TOXIC OR
EXPENSIVE.
BUT FOR MANY THERAPIES THAT'S
NOT AN ISSUE.
I TEND TO FAVOR ETIOLOGIC
QUESTIONS OFFER PREDICTIVE ONES.
THERE'S SETTINGS WHERE
PREDICTION WORKS AND IS KEY BUT
I THINK THEY'RE MORE LIMITED.
>> THANKS.
>> I THINK TO EXPAND A LITTLE
BIT YOUR QUESTION IS PROFOUND
BUT IT'S ANY EXCITING TIME BUT I
THINK THE PUBLIC INTERESTED IN
NUMBERS.
THE PUBLIC IS INTERESTED IN WHAT
ALGORITHMS CAN DO.
FOR ME IT MEANS EDUCATION HAS AN
OPPORTUNITY BECAUSE I THINK WE
CAN'T ANSWER THE WORLD WITH A
TWEET OR AN X, WHATEVER THAT IS
NOW.
AND FOR ME THE KEY CONCEPT I
STRUGGLED WITH FOR A LONG TIME
IS THE BAYESIAN PRINCIPLE THEY
THINK APPLIES TO EVERYTHING THAT
DATA GIVES YOU A LIKELIHOOD
RATIO.
BUT THERE IS A PRIOR AND IF YOU
START WITH INSANE THINGS WITH A
MILLION POSSIBLE THINGS YOU KNOW
NOTHING ABOUT IT'S DIFFICULT TO
GET THE MEAN.
>> THE KEY IS TO BRING THEM
TOGETHER AND HAVEN'T DONE ENOUGH
OF BRINGING BOTH APPROACHES
TOGETHER TRYING TO UNDERSTAND
WHAT'S GOING ON WHEN THEY AGREE
AND TRYING TO UNDER UNDERSTAND
WHAT'S IT GOING ON WHEN THEY
DON'T AGREE.
BUT RIGHT NOW WE'RE SORT OF
ENMANIERED BY THE TECHNOLOGY,
RIGHTFULLY SO.
SO I'M NOT BEING CRITICAL BUT
WONDER IF WE NEED TO SPEND MORE
TIME BRINGING THESE TWO
APPROACHES INTO A SEMI COHESIVE
ALGORITHM OR WORK FLOW.
>> I'D LOVE YOUR ANSWER.
I'VE BEEN STRUCK SOME OF THE
MOST EXCITING THINGS ARE
ANECDOTES OF ONE.
SEQUENCE IS BETTER.
HOW DO WE CONNECT THAT ANECDOTE
TO SCIENCE AND AT A PERSONAL
LEVEL IT'S A LOT OF DATA BUT WE
NEED THE PRINCIPLES OF ACCURACY
AND PREDICTIVE VALUE, ETCETERA.
>> I THINK TO ADD TO THAT,
THERE'S AN INTERESTING POINT ON
TOP OF ERIC TOPOL'S TALK
YESTERDAY ABOUT THE IDEA OF THE
ANECDOTES IS THERE A WAY TO
BRING THEM TO ACTUALLY LEARN
FROM THE END OF ONE STUDIES.
IT SEEMS LIKE THERE MAY BE
POTENTIAL IF SOME TRAINEES ARE
INTERESTED IN PURSUING THAT
ROUTE OF COMING UP WITH A WAY TO
AGGREGATE THESE ANECDOTAL
STORIES SO WE CAN START TO LEARN
ON A LARGER SCALE HOW EFFECTIVE
THESE METHODS ARE.
>> THERE'S AN INTERESTING
QUESTION DIRECTED TO DR. CORESH
BUT I THINK IS RELEVANT TO ALL
THE FOLKS RUNNING THESE STUDIES
WHICH IS ABOUT PARTICIPANT
FATIGUE.
SO WHEN WE HAVE THESE
LONGITUDINAL STUDIES, HOW DO YOU
THINK ABOUT PARTICIPANT FATIGUE.
ARE THERE WAYS TO KEEP PEOPLE
ENGAGED IN WAYS TO ADDRESS FROM
THE OUTSET EXPECTED DROP OFF?
>> YEAH, I'M HAPPY TO START AND
INTERESTED TO HEAR FROM OTHER
PEOPLE HAVE DONE THIS.
I THINK HAVE SHIFTED OUR
INVESTIGATOR GROUP TO THINK MORE
INSTEAD OF OFTEN WE WERE
QUANTIFYING PARTICIPANT BURDEN
IN THE NUMBER OF MINUTES THEY
HAVE TO PUT UP WITH US
ESPECIALLY IN ARIC WITH THE
COHORT IS OLDER I THINK A LOT IS
VALUE.
WHAT ARE THEY GETTING AND THE
PERCIEVED VALUE AND IF THEY LIKE
US THEY'LL LIKE SPENDING TIME
WITH US AND SOME OF US PREFER A
FOUR-HOUR VISIT AND TAKE A
SEVEN-HOUR VISIT THE BURDEN IS
THEN ON US AND THERE'S PROOF
WHERE IN THE ARIC STUDY WE'RE
STRUGGLING TO GET THE RIGHT
RESPONSE RATE BUT IN THE
PARTICIPANTS WHERE WE WENT FROM
TWO TO SIX VISITS A YEAR WE GOT
AT THE END OF THREE YEARS THE
90% RESPONSE RATE BECAUSE WE
GIVE THEM FREE HEARING AIDS AND
SOME GOT AN AGING
INTERDEVELOPINGS.
IT'S AN ENGAGEMENT AND MAKING
SURE THAT WE AS SCIENTISTS SEE
THE TWO-WAY STREET AND SOME
LISTEN TO WHAT THE PARTICIPANTS
WANT IT STUDY.
I THINK THAT'S USEFUL BUT FOR ME
EVEN MORE IMPORTANT IS WHAT
PARTICIPANTS SEE AS IMPACTFUL
AND ENGAGING BUT DIFFERENT.
WE ARE INVESTIGATORS, THEY ARE
PARTICIPANTS.
THEY'RE NOT GOING TO BE ALLIES
IN THE GWAS DATA BUT THEY HAVE
THEIR OWN NEEDS.
I HOPE THAT HELPS.
>> WOULD ANYONE LIKE TO ADD ON
TOP OF THAT?
>> THE ONLY THING I AGREE I WANT
TO ADD IS THAT THE VETERANS IN
GENERAL ARE MORALE TRUISTIC
BECAUSE THEY'RE MORE WILLING TO
CONTRIBUTE ANYTHING THAT HELPS.
THEY KNOW IT MAY NOT HELP THEM
BUT MIGHT HELP THEIR FELLOW
VETERANS.
WHAT WE ARE LEARNING THE
ENGAGEMENT IS VERY IMPORTANT.
THEY NEED TO KNOW WHAT IS COMING
AND WHAT ARE THE CONTRIBUTIONS
THE DATA HAS MADE TO THE SOCIETY
AND TO OTHER VETERANS AND HOW IT
HAS HELPED.
WE HAVE SORT OF TAKEN ENGAGEMENT
AND TALKING AT THE FIELD LEVEL
TO KEEP THEM ENGAGED.
>> IT'S WONDERFUL WE AGREE.
CAN YOU GIVE PEOPLE BALLPARK
ESTIMATES TO CALIBRATE MAY BE
HARD TO DO ON THE TOP OF YOUR
HEAD WHAT ARE REASONABLE
RESPONSE RATES TO A
QUESTIONNAIRE, TO AN EXAM, TO A
BLOOD, TO A TEST AND THEN AS A
REMINDER TO EVERYBODY, YOUR
FOLLOW UP IS 100%.
FOR A LOT OF THINGS THEY DON'T
HAVE A CHOICE.
THEY'RE IN YOUR SYSTEM.
>> EXACTLY.
I CAN TACKLE A FEW.
FOR WHEN WE REACH OUT LIKE THE
OLD SNAIL MAIL LETTER TO FOLKS
YOU SEND OUT A LETTER AND IF
THEY DON'T RESPOND WE SEND A
SECOND AND THIRD.
BETWEEN THE THREE WE GET
SOMEWHERE BETWEEN 50% TO 80%
RATES AND SINCE WE HAVE GONE TO
THE ONLINE THING FOR THEM DOING
THE CONSENT AND GIVING THE BLOOD
IS MORE IMPORTANT THAN ANSWERING
THE SURVEY ACQUISITION AND
THEY'RE LOWER THAN THAT.
WE'RE GEARING UP TO DO A SECOND
SURVEY AND AS YOU SAID THE
E.H.R. DATA IS AVAILABLE ONCE
THE CONSENT IS SIGNED BUT WE'RE
LOOKING INTO COLLECTING A SECOND
SURVEY AND LONGITUDINAL SAMPLE
COLLECTION.
>> IT'S A HELPFUL CALCULATION.
GOD'S GIFT TO COHORTS IS ONCE
ENROLL, FOLLOW-UP IS WHAT
MATTERS AN THAT'S IN YOUR SYSTEM
BUT NOT THE WORLD ONE OF MY
SENIOR MENTORS DID HOUSEHOLD
ENUMERATION GOT 99% OF
HOUSEHOLDS AND IN CHINA YOU CAN
STILL GET HUGE RATES BUT WE HAVE
TO LIVE WITH THE SYSTEMS WE
HAVE.
PASSIVE MAY HELP A LOT.
THANK YOU VERY MUCH.
I THINK WE MAY BE ON TIME,
RIGHT?
>>
>> IT'S A PERFECT TIME TO WRAP
AND RETURNING AT 1:00 P.M.
ENOUGH TIME TO GRAB A QUICK
LUNCH AND KICK OFF WITH DR. BOUR
WINKLE AND THEN WE'LL HAVE AN
AFTERNOON SESSION ON FUTURE
TRENDS, INNOVATIONS APPLICATIONS
OF BIG DATA AND EPIDEMIOLOGY.
IT'SA PLEASURE TO INTRODUCING
DR. BOERWINKLE IS THE DEAN OF
SCHOOL OF PUBLIC HEALTH IN
HOUSTON WITH AN EMPHASIS ON
LARGE TEAM SCIENCE AND THEY'VE
MADE GROUNDBREAKING DISCOVERIES
WITH CHRONIC DISEASE AND
DEMENTIA AND RARE SINGLE GENE
DISORDERS.
HE'S CO-AUTHORED OVER 1,000
PAPERS AND VERY HIGH IMPACT
PAE
PAPERS AND HAS BEEN A WONDERFUL
COLLEAGUE AND MENTOR AND IT'S A
PLEASURE MYSELF TO HAVE BEEN
MENTORED AND LEARNED FROM HIS
AMAZING WORK AND THOUGHTFUL
INSIGHTS AND BROAD WISDOM.
THANK YOU FOR TAKING ON THIS
SLIGHTLY IMPOSSIBLE TASK.
>> THANK YOU.
ORE THE ORGANIZERS IT'S BEEN A
GREAT MEETING AND FUN MEETING.
I'LL TALK ABOUT THE LARGE AMOUNT
OF DATA FOR THE PROGRAM AND
FOCUS MULON MULTIOMICS DATA AND
TALK ABOUT TOOLS AND TECHNIQUES
IN A TWO OF DAY MEETING TOWARDS
THE END THAT COULD BE RATHER DRY
SO I'LL TRY TO WEAVE IN
INTERESTING STORIES IS ALONG
WITH BROAD DISCUSSION AND TAKE
ADVANTAGE OF THE FACT OF BEING A
KEY NOTE SPEAKER TOWARDS THE
AFTERNOON OF THE SECOND DAY I
CAN DO COLOR COMMENTARY OF SOME
OF THE THINGS WE'VE HEARD AN I I
RUN OUT OF TIME I THINK IT'S
MORE INTERESTING.
WE'VE ALL SEEN A NUMBER OF
VERSIONS OF THIS KIND OF SLIDE
WHERE WE HAVE LEVELS OF
INFORMATION UP THIS CASE AS I
SAID ALREADY IN MY CHARGE IT'S
LEVELS OF MULTIOMIC INFORMATION
BUT I THINK IT'S IMPORTANT TO
PAUSE AND THINK ABOUT THESE
KINDS OF FIGURES.
THE FIRST IS IT'S NOT JUST A
RANDOM ORDER.
WE THINK WE'RE SMART AND THAT
INFORMATION IS TRANSMITTED IN
SOME ORDER IN GENOMICS AND
METHYLATION WITH TRANSCRIPTOMICS
AND IT'S LIKELY CORRELATED WITH
PROTEIN LEVELS AND SOMEHOW THE
PROTEINS ARE INTERACTING IN
PATHWAYS AND WE CAN MEASURE
METABOLOMICS AND IT'S A
PHENOTYPE AND ORDER WE PUT
THERE.
AND THERE'S A LONGITUDINAL
COHORT STUDY.
AND IT WAS MENTIONED I THINK JOE
OR DR. CORESH MENTIONED THAT
THESE THINGS CAN CHANGE WITH
DIET.
THEY CHANGE WHEN I STAND UP
VERSUS SITTING DOWN.
SO TIME IS INCREDIBLY IMPORTANT
AND WHEN WE THINK OF DATA
INTEGRATION WHICH IS AN
IMPORTANT PART AND IN THE
MARQUEE OF THIS MEETING IS YOU
HAVE TO KEEP TRACK OF TIME AND
MAKE SURE YOU KNOW WHAT TIME
EACH OF THESE LEVELS BIOLOGIC
HIERARCHY WERE MENTIONED.
AND FINALLY TISSUE.
IN TISSUES WE'RE ALL IN LOVE
WITH OUR SOLID TISSUES SO WE'RE
LIMITED IN WHAT WE CAN GET BUT
ON THE OTHER HAND WE'VE GOTTEN
CLEVER I WOULD SAY ABOUT GETTING
INFORMATION FOR EXAMPLE FROM
STOOL SAMPLES.
WE'VE GOTTEN CLEVER WHAT WE CAN
GET WITH SMALL BIOPSIES OR
SAMPLES FOR EXAMPLE IN THE
CANCER ARENA SO IF WE CAN NEST
SOME OF THOSE SOME OF THAT
ABILITY IS INFORMATIVE AND
DR. PSATY TEE UP THE BALL NICELY
BUT MAYBE IT'S A SIGN OF OLD AGE
I LIKE UNDERSTANDING OF A
QUESTION EVEN THOUGH I
UNDERSTAND MODERN DAY SCIENCE
CAN FIND RELATIONSHIPS THE MERE
MORTAL COULDN'T HAVE SEEN BUT
IT'S STILL IMPORTANT TO PUT THE
OBSERVATIONS IN THE CONTEXT OF
WHAT'S DRIVING YOU TO DO THIS.
IT GOES BACK TO FOR THOSE WHO
HEARD THE WELCOME AND THERE WAS
A NICE DISCUSSION OF BRINGING
TOGETHER HYPOTHESIS QUESTION
DRIVEN VERSUS HYPOTHESIS FREE OR
A.I. SCIENCE AND I THINK A
CHALLENGE WE'RE GOING TO HAVE AS
A FIELD THE NEXT FEW YEARS SHOW
WE BRING THOSE THINGS TOGETHER.
WOULD TALKED ABOUT PARTICIPATING
RATES AND WHAT PERCENTAGE OF
INDIVIDUALS TAKE PART IN THE
SURVEY AND THERE'S THE CONCEPT
OF MISSING OR INCOMPLETE DATA.
IT'S IMPORTANT TO UNDERSTAND
WHAT IS UNDERLYING THE
MISSINGNESS AND WHETHER IT'S
RANDOM OR NOT RANDOM.
THAT'S AN OBVIOUS THING TO SAY
BUT HOW YOU HANDLE AND INTERPRET
THE MISSINGNESS IT'S BECAUSE THE
FIELD BUILT TOOLS IN THE CONTEXT
AND UNDERSTAND WHERE THE ANSWER
FITS IN AND WHEN IS IT WISE TO
IMPUTE AND NOT IMPUTE.
MAYBE WE'RE SMARTER THAN WE
REALLY ARE.
I WORK IN A FIELD WITH
METABOLOMICS DATA WE KNOW SOME
LEVELS ARE NOT BELOW THE
DETECTION LIMIT AND NEED TO
UNDERSTAND WHAT'S DRIVING THAT.
COMPUTING HAS GROWN ENORMOUSLY
IN THE LAST 10 OR FIVE YEARS
CERTAINLY BUT THERE'S A PROBLEM
EMERGING WE'RE PART OF THE
PROBLEM.
WE BUILT DATA ENCLAVES WHERE
DATA IS HELD AND BECAUSE IT'S
LARGE, SOMETIMES PETABYTES OF
DATA IN A CLOUD ENVIRONMENT
BECAUSE OF COMPUTATIONAL
EFFICIENCY BUT YOU CAN'T
DOWNLOAD IT AND CAN'T COMBINE IT
WITH OTHER SOURCES AND NIGMS HAS
ANOTHER PLATFORM AND NHGRI HAS
PLANVILLE AND AGING HAS NIGADS.
THE "ALL OF US" DATA AND THEY'RE
EACH IN ENCLAVES, ONE, YOU CAN'T
BRING THE DATA TOGETHER IS THAT
BEST FOR THE SCIENCE IN THE
UNITED STATES.
NUMBER ONE.
NUMBER TWO, THERE'S AN
OPPORTUNITY FOR DATA SCIENTISTS
TO THINK ABOUT MAYBE YOU DON'T
NEED IT BRING THE DATA TOGETHER
THERE'S THE SUFFICIENT STATISTIC
IS THERE A WAY TO SUMMARIZE AND
NOT LOSE INFORMATION BRING THAT
STATISTIC DOWN AND IN SYMPOSIUMS
LIKE THIS WE NEED TO THINK ABOUT
ENCLAVING DATA.
WE'RE SHARING IT BUT ONLY IN
PARTICULAR COMPUTE ENVIRONMENTS
AND YOU CAN'T CROSS COMPUTE
ENVIRONMENTS.
NEED TO BRING THE TOOLS AND
APPRO
APPROA
PROXIMAL AND IT WAS DONE WITHIN
THE CONTEXT WITHIN BUILDING
COMMUNITIES OF INVESTIGATORS AND
THEN THERE'S THE DISTRIBUTION OF
DATA WE'RE NOT DOING A GOOD JOB
OR ENOUGH THOUGHT IN IT FOR ONE.
HERE'S A LIST OF METHODS AND
PRETTY DRY STUFF.
WHEN WE HAVE DATA OFTEN ONE
CHALLENGE IS INDIVIDUALS TRYING
TO THINK OF DATA REDUCTION
TECHNIQUES.
THE FIRST TWO ARE CLUSTER AND
FACTOR ANALYSIS AND TAKING LARGE
VARIABLES AND TRYING TO EXTRACT
A PRINCIPLE COMPONENT, IF YOU
WILL, AND WORK WITH THE SMALLER
NUMBER AND THERE'S ADVANTAGES IN
TERMS OF COMPUTATIONAL
EFFICIENCY.
THERE'S ADVANTAGES WHEN WE THINK
WE UNDERSTAND WHAT'S HAPPENING
TO INTERPRET THE PRINCIPLE
COMPONENTS IN SOME WAY.
WITH BIOLOGY AND THERE'S ONE NOW
PARTICIPANTS AND FOR SOME WHO
HAD SOME FORMAL TRAINING YOU
HAVE PROBABLY RAN ACROSS THE
CANONICAL CORRELATION BUT
THERE'S GROWING INTEREST THIS
CONCEPT OF ANALYSIS.
THINKING BACK TO MY SECOND SLIDE
LAYERS OF OMICS AND THINK OF THE
CANONICAL CORRELATIONS WITHIN
THE LAYER AND RELATE THEM ACROSS
LAYERS.
IT'S A METHOD THAT IS GROWING IN
THE FIELD GROWING AND TRUST AND
OMICS AND THIS I DON'T HAVE
ASSOCIATION WITH WHICH PROBABLY
GIVES IT MORE CREDIBILITY.
IT'S WORK IN BRAIN IMAGING AND
FUNCTIONAL BRAIN ANALYSIS BY
WANG ET AL. AND PUBLISHED IN
NEURO IMAGING AND WANT TO
COMPLEMENT THE JOURNAL THAT GAVE
THESE AUTHORS A LOT OF LICENSE.
WHAT THEY'VE DONE IS WALKED
THROUGH COMPLICATED ANALYSIS AND
EXPLAINED WHAT THEY WERE DOING
AT EACH STEP AND TOOK TIME
SERIES DATA OF SIGNALS AND
THAT'S ONE MEASURE AND ACROSS
TIME AND THEN THEY ALSO HAD BY
QUESTIONNAIRE AMONG A NUMBER OF
PSYCHO METRIC MEASURES THEY
AGAIN USED CANONICAL
CORRELATIONS TO BOIL DOWN TO
SOMETHING THEY PUT LABELS ON,
MOOD, PSYCHOSIS, FEAR AND
EXTERNALIZING BEHAVIOR.
THEY TOOK THE SURVEY DATA AND
BROUGHT THEM TOGETHER ON THE
LOWER RIGHT.
I WASN'T GOING TO GO THROUGH THE
SCIENCE OR RESULTS OF THIS BUT I
DO THINK AND ENCOURAGE FOR THOSE
WHERE THIS IS A RELATIVELY NEW
PAPER YOU CAN READ THE GO, THE
BAD, THE LOGIC OF THE WORK AND
IT'S A MARVELOUS EXAMPLE OF
PRESENTING SCIENCE AND LEARNING
AT THE SAME TIME.
IT'S IMPORTANT TO THINK ABOUT
WHEN YOU EXERCISE CERTAIN
STATISTICS AND CAME UP DURING
THE MEETING, WHY ARE WE DOING
THIS?
AT THE END OF THE DAY YOU SIT
DOWN ON THE PORCH WITH YOUR ICE
TEA, ARE WE TRYING TO IMPROVE
PREDICTION OR MECHANISTIC
UNDERSTANDING OR ETIOLOGY?
THINK BACK TO THE LAYERS AND THE
QUESTION IS WHEN DO I EXERCISE
THE STATISTICAL METHODS TO DO
DATA REDUCTION?
WHEN DO I DO THE PRINCIPLE
COMPONENTS?
WHEN DO I DO THE CANONICAL
CORRELATION AND THOSE ARE TWO
AREAS ONE IS YOU DO THE
CANONICAL REDUCTION OR DATA
ANALYSIS AT EACH LEVEL AND THEN
LOOK AT THE RELATIONSHIP BETWEEN
LEVELS.
I THINK PROBABLY YOU CAN SEE OR
FEEL THE METHOD HELPS IMPROVE
UNDERSTANDING.
IF YOU'RE TRYING TO UNDERSTAND A
GIVEN PROBLEM OR WHY THERE MAY
BE A RELATIONSHIP THIS IS A
PREFERRED METHOD.
IN ALL YOU'RE TRYING TO DO IS
IMPROVE PREDICTION AND REALLY
DON'T CARE ABOUT UNDERSTANDING,
WHICH IS PROBABLY UNFORTUNATELY
MOST OF US WANT IT DO BOTH AT
THE SAME TIME PROBABLY, YOU
DON'T NEED TO WORRY ABOUT THE
LAYERS.
YOU'RE THE ONE WHO DEFINED THE
LAYERS AND PUT THEM IN ORDER.
YOU CAN CAPA -- CANCAPANATE ALL
THE DATA AND YOU CHOOSE WHEN TO
DO THE DATA REDUCTION METHODS.
IS THEN ALL THE EXAMPLES WE
HEARD AND THEY ALL HAD SOME SORT
OF GENETICS WITH OMICS WITH
OTHER PHENOTYPIC
CHARACTERIZATION.
THAT ALLOWS US TO ASK WHAT IS
SPECIAL ABOUT GENETIC DATA AND
HOW DO WE LEVERAGE THAT.
AND WHAT'S SPECIAL ABOUT IT IS
THERE'S ELEMENT OR LITTLE CHANCE
FOR REVERSE CAUSATION AND IN
CANCER IN PARTICULAR WHERE THE
OUTCOME OR PHENOTYPE WILL
INFLUENCE THE GENOTYPE.
AND SO THIS ALLOWS ONE AND BRUCE
USED THIS WORD OF MENDELIAN
RANDOMIZATION UNDERSTANDING THE
RELATIONSHIP BETWEEN THE
PARTICULAR OMIC IS IT CAUSAL OR
INFLUENCING IS THERE A
DIRECTIONALITY IN THE OUTCOME OR
IS THERE POSSIBLE REVERSE
CAUSATION.
OVER THE LAST 10 YEARS IT'S
GROWN TO A RELATIVELY SIMPLE
CONCEPT TO AN EXTREMELY
COMPLICATED MEDIATION SET OF
ANALYSIS.
I'M NOT GOING TO WALK THROUGH
IT.
IT GETS TEDIOUS BUT BASICALLY
EVERYONE CAN SEE AT THE SIMPLE
LEVEL HOW THE INSTRUMENTAL
VARIABLES AND MENDELIAN
RANDOMIZATION AND UNDERSTAND
WHETHER A GIVEN ASSOCIATION
BETWEEN AN OMIC MEASURE
PROTEOMIC OR METABOLOMIC MEASURE
IS LIKELY OR POSSIBLY CAUSAL OR
IS THERE REVERSE CAUSATION.
USUALLY WE HAVE MANY MEASURES.
AND YOU CAN LOOK AT HIGH
DIMENSIONAL METHODS OF REALLY
IT'S THE FIRST FUNDAMENTAL
MENDELIAN RANDOMIZATION BUT ON
STEROIDS AND THERE'S THE CONCEPT
OF LATENT MEDIATION THERE'S A
PUB MED I.D. ALONG WITH THE TOOL
TO HELP IN THOSE ANALYSES.
IF YOU'RE A PERSON THAT HAS A
LOT OF OMICS DATA WITH
METABOLOMICS IT POSSIBLEY IS A
METHOD YOU WOULD BE EXERCISING.
THERE'S OMICS DATA AND IT'S BEEN
A LOT OF FUN TO SEE THE
EVOLUTION OF THE OMICS DATA
BEGINNING WITH GENETIC MARKERS
MOVING INTO WHOLE GENOME
SEQUENCE.
ADDING ON METHYLATION BUT
RECENTLY METABOLOMICS AND
PROTEOMICS AND IT'S A CHALLENGE
AND OPPORTUNITY.
ONE, EXPAND LOOKING AT MARKERS
OF DISEASE PROGRESSION.
WARE VERY GOOD IN AT LEAST IN MY
CAL AWAY THINK WE'RE GOOD AT
MEASURING THE GENOME BUT NOT AS
>> GOOD AS WE SHOULD BE IN
MEASURING ENVIRONMENTAL
EXPOSURE.
SOME OF THE OTHER OMICS ARE
EITHER GOOD MEASURES OF
ENVIRONMENTAL EXPOSURES OR GOOD
MEASURES OF GENE INTERACTION AND
I ENCOURAGE THINKING ABOUT THE
ENVIRONMENT.
I MADE A LIVING IN GENETIC
EPIDEMIOLOGY BUT I DO THINK IT'S
AN OPPORTUNITY HERE TO EXPAND
BEYOND JUST SIMPLE GENOMICS AND
THINK OF GENE ENVIRONMENT
INTERACTION WITH THIGHS OTHER
OMICS.
I'M GOING TO GIVE YOU A COUPLE
EXAMPLES OF TOUCHING ON A
VARIETY OF THINGS.
MOST OF THE DATA I'M GOING TO
PRESENT SO I DON'T BOUNCE AROUND
WILL BE FROM THE EXAMPLE -- WE
CALL VISIT 5 TO PUT A DATE ON IT
IS 2011.
ARIC IS A STUDY AND THIS ONE IS
ABOUT 5,500 WHERE WE HAVE GOOD
MEASURES OF GENOMICS, WE HAVE
TRANSCRIPT OMICS AND METHYLATION
DATA AND PROTEOMIC DATA AND
METABOLOMIC DATA AT THE ONE TIME
POINT.
THAT ALLOWS US TO DO A LOT OF
WORK IN TERMS OF LEVELS OF SOME
BIOLOGIC HIERARCHY AND LOOKING
FORWARD TO PREDICT DISEASE THAT
I THINK JOE NICELY SET UP FOR
US.
I'LL GIVE A COUPLE EXAMPLES.
THE FIRST COMES FROM HEART
FAILURE.
I WANT TO GIVE A SHOUT OUT THE
U.K. BIO BANK HAS HAD
PUBLICATIONS ONE IN NATURE AND
MEDICINE IN 2022 AND WE HAVE THE
OPPORTUNITY AND SHUNT FORGET
GOOD EPIDEMIOLOGY NOT GOOD,
GREAT EPIDEMIOLOGY THAT'S COME
BEFORE US.
BEFORE YOU GO NUTS AND START
DOING A LOT OF MULE OMIC --
MULTIOMIC AND PREDICTION LET'S
NOT FORGET AGE, GENDER AND
COMORBIDITIES PARTICULARLY IF
YOU'RE STUDYING HEART FAILURE,
HYPERTENSION, DIABETES.
THERE'S A GOOD BIOMEDICAL AND
MOST ARE AWARE OF PRO V NP A
MARKER OF CARDIAC INJURY.
I WANT TO MAKE A PLUG AND GIVE
CREDIT TO THOSE WHO CAME BEFORE
US AND INCORPORATE THE
FUNDAMENTAL EPIDEMIOLOGIC RISK
FACTORS AND GOING FORWARD.
THIS IS A RATHER NOISY SLIDE.
IT'S BASICALLY LOOKING AT
METABOLOMICS AND INCIDENT HEART
FAILURE.
THERE'S ALL-CAUSE HEART FAILURE
AND FOR THE OAFICIONADOS IN THE
GROUP YOU'LL SEE ON THE PATHWAYS
BY METABOLOMICS AND IT'S
INTERESTING TO NOTE AND THESE
ARE NOT IN ANY ORDER TO MAKE THE
DOTS LOOK A CERTAIN WAY AND LOOK
AT THE AMINO ACIDS AND SOME ARE
POSITIVELY ASSOCIATED WITH
DISEASE AND WHEN YOU GET TO THE
LIPIDS, THE GREEN IN THE MIDDLE
THERE'S A CLASS OF LIPIDS FATTY
ACIDS POSITIVELY AND NEGATIVELY
ASSOCIATED WITH DISEASE.
IT'S INTERESTING TO SEE THE
PATTERNS GOING FORWARD.
AND WHAT WE'VE DONE WE HEARD
ABOUT GENETIC RISK SCORES.
YOU CAN CREATE BASICALLY
METABOLOMIC RISK SCORES AND YOU
CAN ASK WHETHER THESE ARE ROC
CURVES YOU'RE ALL USED TO AND
LOOK AT THE ONES ON THE LEFT AND
THE TRADITIONAL RISK FACTORS OR
THE TRADITIONAL RISK FACTORS
PLUS PRO VNP IN BLUE AND I GUESS
IT'S LAVENDER AND YOU SEE THE
INCREASE IN PREDICTABILITY WHEN
YOU ADD METABOLOMIC RISK SCORES.
I'M GOING THROUGH THIS BECAUSE
IT'S ANOTHER WAY OF DATA
REDUCTION TAKING ALL THE
COMPLICATED THINGS ON THE
PREVIOUS SLIDE FOR EACH
INDIVIDUAL COMING UP WITH A
NUMBER AND USING THAT NUMBER IN
A PREDICTION ANALYSIS.
AND LIKEWISE THERE'S TWO
CONCEPTS ON THE SLIDE.
ONE IS TAKING THE RESULTS FROM
U.K. BIO BANK AND ADDING THEM IN
AND SEEING IF THE ARIC
METABOLOMIC RISK SCORE IMPROVES
PREDICTION.
THAT'S ONE QUESTION.
THE SECOND IS IF YOU HAVE SERUM
METABOLOMICS, DOES ADDING
URINARY METABOLOMICS TO YOUR
REPERTOIRE DOES IT IMPROVE YOUR
ABILITY TO PREDICT.
I'LL CUT TO THE CHASE MAYBE ALL
THE WAY TO THE RIGHT, THOSE ARE
STATISTICS ON THE Y AXIS AND
NOTICE THE STEP WISE INCREASE
AND I WAS A BIT SURPRISED TO BE
HONEST WITH YOU THAT URINARY
METABOLOMICS IMPROVED.
TRANSITION BEING FROM ONE STUDY
AND THIS PROGRAM HAS BEEN
MENTIONED OF THE TOP MED
PROGRAM.
IT'S UNIQUE IN A NUMBER OF WAYS.
ONE IS VERY SPECIFICALLY TRYING
TO GET MULTIOMIC DATA IN NOT
ONLY A LARGE SAMPLE BUT A
DIVERSE SAMPLE.
IT CONTAINS THE LARGE COHORT
STUDIES MENTIONED MULTIPLE
TIMES.
ARIC, FRAMINGHAM, THE STUDY OF
LATINOS, ETCETERA AND ALSO
INCLUDES A NUMBER OF DISEASE
SPECIFIC PARTICULARLY IN THE
LUNG.
A NUMBER OF DISEASE SPECIFIC AND
WHAT'S SHOWN IN THE PIE DIAGRAM
IS THE ETHNICITY BUT THE
MAJORITY TODAY IS STILL
NON-HISPANIC WHITES.
THERE'S A LOT OF EFFORT TO MAKE
SURE WE HAVE GOOD REPRESENTATION
OF AFRICAN AMERICANS, LATINOS.
I CAN'T REMEMBER WHO IT WAS.
WE'RE STILL NOT DOING AS WELL AS
WE SHOULD IN ASIAN ANCESTRY.
I KNOW THERE'S AN ASIAN COHORT
BEING LAUNCHED AND IT'S OVER
DUE.
IT IS BY ITSELF A DIVERSE GROUP.
>> YOU WARNED THAT WAS GOOD
WISDOM AT THE BEGINNING BUT AT
THE COST OF THE LATE SLIDES BUT
WE'RE AT THE WRAP-UP, PHASE.
THANK YOU.
>> IT'S A GOOD TIME TO WRAP UP
AND SHOWS METABOLOMICS IN A
GWAS.
I'LL SKIP THAT.
I WANT TO PAUSE.
THIS IS DRILLING DOWN ON A GWAS
OF THE ENTIRE HUMAN PROTEOME.
BUT SPECIFICALLY THEN LOOKING AT
THE RESULTS OF THE FINDINGS HOW
THEY RELATE TO VARIOUS DISEASES
IN THIS CASE SLEEP.
SO IT'S A NICE EXAMPLE BASICALLY
AGAIN OF A WELL CHARACTERIZED
COHORT OF THE ABILITY TO DO
DISCOVERY WORK IN A LAYER THAT'S
CLOSE TO THE GENE LEVEL AND THEN
TAKE THOSE FINDINGS AND RELATE
IT TO A PHENOTYPE AS I SAID THIS
IS WITH SLEEP.
IT SHOWS THE APOE ASSOCIATED A
LEUCINE RICH PROTEIN ASSOCIATED
WITH SLEEP PATTERNS.
THIS IS A FUN SLIDE AND MY LAST.
I CALL THIS A VIRTUOUS CYCLE AND
IT'S WORK -- I DON'T KNOW IF
JOSH DENNY IS STILL ON.
IT'S WORK I TRIED TO PROMOTE
PARTICULARLY WITHIN THE ALL OF
US IS TRYING TO CREATE A
VIRTUOUS CYCLE WHERE WE HAVE
POPULATION DATA AND COHORT DATA.
WE DO A LOT OF DISCOVERY WORK ON
THE YELLOW AND GREEN AND CREATE
MOLECULAR PROFILES OF INDIVIDUAL
PATIENTS AND FEED IT BACK TO
CLINICAL CARE AND HAVE THE
CLINICAL CARE THEN INFORM THE
COHORT STUDIES IN THE SAME
POPULATION.
I THINK IT'S AN IDEAL WE SHOULD
ALL STRIVE FOR THOSE WHO ARE
EMBEDDED WITHIN UNIVERSITIES OR
INSTITUTIONS.
THEY'RE ASSOCIATED WITH HEALTH
CARE AND IT'S A NICE SYSTEM TO
PROMOTE THIS WORK.
A WONDERFUL THANK YOU AND THE
PARTICIPANTS AND STAFF AND THE
INVESTIGATORS AND I WANT TO
THANK NHLBI BOTH FOR THIS
MEETING AND FOR OTHERS AT NIH
AND THE CHANCE TO BE HERE.
>> WONDERFUL ERIC.
BEAUTIFUL TALK.
I HOPE PEOPLE APPRECIATED THE
WORDS OF WISDOM BROUGHT ABOUT
ANALYSIS AND THINKING HOW TO DO
LAYERS.
I'LL ASK ONE QUESTION FOR THE
SAKE OF TIME.
MENTIONED CLOUD CAUSED SILE
OWING FOR ENCLAVES.
CAN THE CLOUD BE PART OF THE
SOLUTION?
>> IT CAN BE.
I'LL GIVE YOU AN OBSERVATION I
THINK OVER TIME SCIENTISTS AND
INVESTIGATORS HAVE BECOME
WONDERFULLY IMPROVE AT
COLLABORATING.
WE LEARNED HOW TO COLLABORATE
WITH ONE ANOTHER AND WE MIGHT BE
SOMETIMES COMPETITORS ON TUESDAY
AND ON THURSDAY WE'RE
COLLABORATORS.
SCIENTISTS HAVE LEARNED TO DO
THAT VERY WELL.
NIH INSTITUTES LEARNED TO DO
THAT AND WE NEED MORE
COLLABORATION AND FIGURE OUT HOW
TO SHARE.
THAT'S ONE OBSERVATION.
THE OTHER IS THE CLOUD CAN BE
THE SOLUTION.
THERE'S A LOT HAPPENING IN THE
PRIVATE SECTOR AND FEES ARE VERY
HIGH.
THE OTHERS ARE BUILDING TUNNELS.
IF WE HAD BETTER TUNNELLING
AVAILABLE TO THE PUBLIC OR
INVESTIGATORS LIKE US WOULD
HELP.
YES, THEY'RE PART OF THE
SOLUTION.
>> WONDERFUL.
THANK YOU.
AND YOU KNOW PEOPLE CAN SUBMIT
QUESTIONS AND YOU CAN ANSWER.
SO CAN YOU QUEUE UP THE NEXT SET
OF SLIDES AND I'LL INTRODUCE
DR. MOFFITT AN ASSOCIATE
PROFESSOR IN THE DEPARTMENT OF
HEMATOLOGY AND MEDICAL ONTOLOGY
THE SECONDARY APPOINTMENT IN
BIOMEDICAL INFORMATICS AT EMORY
UNIVERSITY AND I SEE YOU ON
CAMERA.
WONDERFUL, THANK YOU.
HIS EARLY WORK APPLIED MAJOR
TECHNIQUES TO DISCOVER CANONICAL
SUB TYPES OF CANCER AND HE'S
BEEN PART OF THE NATIONAL COVID
COHORT COLLABORATE WHICH CREATES
A COMMUNITY OF OVER 4,000 USERS
WORKING WITH HEALTH RECORDS OF
OVER 20 MILLION PATIENTS AND
BACK IT COLLABORATION.
TAKE IT AWAY.
>> SO I'M GOING TO TALK ABOUT
HOW WE'RE ABLE TO LEVERAGE THIS
DATA MULTI-INSTITUTE DATA AND GO
TOWARDS BUILDING MACHINE
LEARNING MODELS OF DISEASE.
SOME STATS WHY THE N3C OR
NATIONAL COVID COHORT
COLLABORATE.
IVE.
WE HAVE E.H.R. RECORDS FROM
ABOUT 20 MILLION PATIENTS AND 8
MILLION ARE CONFIRMED TO BE
COVID POSITIVE.
THE DATA COMES FROM 81 SITES.
WE MEAN HEALTH CARE SYSTEMS.
SO IT'S OUR STATEWIDE H.I.E.s
AND THERE'S BEEN MAYOS AND THEY
HAVE DATA FROM THEIR SITES
ACROSS DIFFERENT STATES AND THE
E.H.R. GIVES 27 BILLION ROWS OF
DATA TO DO COMPUTATION ON.
THE DATA THAT COMES IN COMES
FROM THE SITES' OWN DATA MODEL
IN SOME OTHER COMMON DATA MODEL
WHICH WE THINK HAS BEEN
IMPORTANT FOR GETTING GOOD
PARTICIPATION FROM THE
COMMUNITY.
BECAUSE WE TAKE THE DATA AS IT
WAS AT A HEALTH CARE SITE, WE
TRANSIT INTO THE OMA COMMON DATA
MODEL WITH A SERIES OF MAPPINGS.
AND THERE'S LOTS OF DATA QUALITY
CHECKS WE LOOK TO THE COMMUNITY
TO SEE IF IT GETS PASSED FOR
DATA ANALYSIS AND TO GIVE
FEEDBACK BACK TO THE SITES THAT
PROVIDE DATA IF THERE'S
DEFICIENCY WE CAN HELP THEM FIX
IT AT THE SOURCE WHICH IMPROVES
THE DATA IN N3C AND BACK AT THE
ORIGINAL SITE.
YOU CAN GOOGLE N3C AND YOU CAN
GET DETAILS AND CODE AND
BASICALLY WE TAKE ALL THE COVID
POSITIVE PATIENTS AT A SITE AND
2-1 MATCH DEMOGRAPHICALLY WITH
COVID NEGATIVE PATIENTS.
SO THE TYPE OF DATA WE HAVE IS
THE COMMON DATA MODEL AND HAVE
TABLES FOR PERSON, DRUG,
PROCEDURE, CONDITION,
MEASUREMENT.
WE HAVE ALL THE DEMOGRAPHIC
INFORMATION YOU'D WANT TO KNOW
ABOUT A PATIENT.
THEIR AGE, GENDER, ALL THAT.
AND THERE'S DIAGNOSIS AND BY
MEASUREMENT WE MEAN LAB
MEASUREMENT AND NOT ONLY DO WE
HAVE THE TIMING OF THE LABS BUT
WE HAVE THE RESULTS.
ONE OF THE KEY HARMONIZATIONS WE
DO INSIDE OF N3C IS MAP THE
COMMON MEASUREMENTS TO A COMMON
UNIT SPACE.
SO WHEN YOU GRAB WEIGHT OR
SODIUM THEY'RE ALL DEALING WITH
ONE PARTICULAR UNIT ACROSS THE
ENTIRE DATA SET SO USERS DON'T
HAVE TO DEAL WITH THAT.
IN ADDITION TO THE TYPICAL DATA
YOU GET FROM AN E.H.R. ABOVE,
WE'VE ALSO BEEN ABLE TO GET
ENHANCEMENTS EXTRACTING THINGS
FROM HAND WRITTEN NOTES FROM NLP
AND WE DON'T GET THE NOTES
THEMSELVES BUT WE GET THINGS
EXTRACTED FROM THE NOTES LIKE
CERTAIN DIAGNOSES, COUGH, THINGS
THAT DON'T SHOW UP AS A
CONDITION BUT SYMPTOM CAN GET
PICKED UP BY NATURAL LANGUAGE
PROCESSING.
AND WE HAVE DATA ON DEATH
OUTSIDE THE HOSPITAL TO INCREASE
OUR MORTALITY AND CNS CLAIMS
DATA TO SEE STUFF MAYBE NOT
PICKED UP BY A PARTICULAR HEALTH
SYSTEM BUT PEOPLE ON MEDICARE
AND MEDICAID AND MORE PROCEDURES
THAN MEDICATIONS.
IT'S A GREAT RESOURCE OF DATA
AND THERE'S RESTRICTIONS HERE
BECAUSE IT'S E.H.R. DATA.
THE FIRST IS IT HAS TO BE COVID
RELATED ONLY.
NO ATTEMPTS TO REIDENTIFY
PATIENTS THOUGH THE DATA IS
COMPLETELY DEIDENTIFIED EXCEPT
FOR DATES AND FIVE-DIGIT ZIP
CODES.
HIPAA LIMITED IN THAT WAY.
THE SOFTWARE FOR THE PLATFORM
PREVENTS USERS FROM DOWNLOADING
OR CAPTURING THE RAW DATA.
NOT ONLY ARE YOU DISALLOWED,
YOU'RE ALSO NOT CAPABLE OF
DOWNLOADING THE DATA.
THE DATA COMES IN TWO TIERS OF
ACCESS.
ONE IS THE HIPAA LIMITED WHERE
YOU HAVE DATES AND LOCATIONS.
AND THERE'S ALSO A SAFE HARBOR
VERSION WHERE THE DATES HAVE
BEEN SHIFTED AND THE ZIP CODES
HAVE BEEN OB OBFUSCATED AND ONE
ADVANTAGE OF THE LOCK DOWN OF
THE DATA IS COMPLETE ANALYTICAL
PROVIDENCE AND IT'S TRACEABLE
BACK TO THE ROOTS.
USERS CAN EVEN DIG UP HILL IN
THE MAPPING STEPS TO SEE ALL THE
CODE AND TRANSFORMATIONS FOR
EXAMPLE TO TAKE SOMETHING FROM
AN ACT AND MAP IT TO OMAM AND
IT'S TRANSPARENT AND TRACKABLE
ALLOWING PEOPLE TO SHARE CODE
ACROSS QUITE TRANSPARENT.
I'M GOING TO TALK ABOUT THINGS
THAT HAVE BEEN DONE IN THE PAST
TO GIVE THE IDEA OF THE RICHNESS
OF AVAILABLE FOR USE AND CAN
INSPIRE QUESTIONS YOU WANT TO DO
YOURSELF.
ON THE LEFT IS A FIGURE FROM THE
INITIAL COHORT CHARACTERIZATION
PAPER WE PUT OUT.
YOU'LL PUT OUT THERE'S NOT EVEN
A YEAR DOWN THERE BECAUSE IT WAS
2020 AND WE'RE LOOKING AT
SEVERITY OVER TIME AND GENERALLY
YOU CAN SEE THE BARS ARE GOING
DOWN AS WE GO OUT TO THE RIGHT
WHICH WAS A HOPEFUL AND PERHAPS
REASSURING TREND THE SEVERITY OF
PATIENTS INFECTED WITH COVID WAS
GOING DOWN.
PATIENTS MILD WOULD BE LIKE OUT
PATIENTS, MILD IN THE E.D. AND
SEVERE IS YOUR HOSPITALIZED AND
YOU NEED SOME INVASIVE
VENTILATION AND ONE NICE THING
ABOUT BUILDING THE PIPELINES IS
AS THE NEW DATA'S COMING IN WE
CAN RERUN THE PIPELINES ON THE
SCHEDULE AND DATA COMES IN TO
N3C PERIODLY FROM PROVIDING
SIGHT BUT A NEW CUT OF THE DATA
IS GIVEN TO RESEARCHERS ONCE A
WEEK.
EVERY WEEK WE CAN UPDATE OUR
RESULTS AND IN FACT ABLE TO TAKE
THE SNAPSHOT PUBLISHED IN THE
PAPER AND TURN IT INTO A
DASHBOARD WHERE WE'RE ABLE TO
SEE UNFORTUNATELY THE TREND OF
DECREASING SEVERITY ABOUT THE
TIME WE PUBLISH THE PAPER
STARTED CREEPING UP AS DELTA
CAME TO DOWN.
AND IF YOU GO TO COVID.CT2H.org
YOU'LL FIND MET INFORMATION
ABOUT THE ENCLAVE AS WELL AS THE
ACTUAL DATA UNDER.
WE HAVE INFORMATION ON
MEDICATION AND YOU SEE THE
FRACTION OF PATIENTS AT THE
VARIOUS SEVERITY LEVELS AND
PATIENTS HOSPITALIZED AND EITHER
REQUIRED VENTILATION OR ENDED UP
SUCCUMBING TO DISEASE.
YOU SEE AZITHROMYCIN WAS POPULAR
AND MELLOWED OUT AND
HYDROXYCHLOROQUINE WAS POPULAR
IN MARCH AND APRIL AND YOU CAN
SEE REMDESIVIR RISING UP AND
GENERALLY SYSTEMIC IN BACTERIALS
WANED AND YOU SEE DEXOMETHOSONE
AND YOU SEE THE MASSIVE NUMBER
OF PATIENTS WE CAN ASK THE
QUESTIONS ABOUT.
HERE ARE GRAPHS IF YOU LOOK AT
THE X AXIS THAT IS DAYS OF
VISITS.
SO ZERO IS THE DAY THEY SHOWED
UP TO THE HOSPITAL AND AS WE GO
OUT TO THE RIGHT AS PATIENTS WHO
ARE STAYING LONGER AND LONGER.
PATIEN
PATIENTS IN BLACK ENDED UP DYING
AND PATIENTS IN RED NEEDED
VENTILATION BUT DIED AND YELLOW
ARE JUST HOSPITALIZED PATIENTS
AND LINE IN GREEN ARE PATIENTS
IN THE E.D. WHICH IS WHY THEIR
STAYS ARE SHORTER.
AND LOOK AT THE VALIDITY FOR THE
VITAL SIGNS AND INTERESTING
TRENDS BASED ON THOSE ABLE TO
RECOVER VERSUS THOSE WHO DIED IN
THE RED AND BLACK FOR EXAMPLE
LIKE RESPIRATORY RATE AND
ELEVATED RESPIRATORY RATE AND A
DIP AS THEY'RE INITIALLY BEING
TREATED WITH PATIENTS WHO
EVENTUALLY DIED AND THE RATES
DRIFT UP AND NEVER GET BACK IN
CONTROL.
WE ALSO HAVE THE OTHER
TRADITIONAL LABS.
IT'S A SET OF SIX.
LITERALLY ANY LAB WE SHOULD HAVE
THE RESULTS OF.
HERE WE HAVE THE CRP MEASURES
AND YOU CAN SEE INTERESTING
TRENDS WHICH ILLUSTRATE THE
VOLUME OF DATA UNDERNEATH THAT
ARE ABLE TO TEASE OUT
INTERESTING DIFFERENCES IN THE
POPULATIONS OVER TIME.
I WANT TO WAS ON ONE PARTICULAR
STUDY FROM COLORADO AND HE'S
LOOKING AT THE DIFFERENCE
BETWEEN MISC.
THEY'RE ALL PEDIATRIC PATIENTS
WITH THE BLUE GRAY AND PEDIATRIC
PATIENTS MERELY EXPERIENCING
COVID INFECTION.
AND IF WE LOOK AT THE BOTTOM
LEVEL CIRCLED AND THESE ARE
COMORBIDITIES OF INTEREST IN THE
PEDIATRIC POPULATION AND IF YOU
COMPARE LEFT TO RIGHT WE DON'T
SEE MUCH DIFFERENCE IN THE SIZE
OF THE BARS OR NUMBERS WHICH ARE
PERCENT OF PATIENTS WITH THE
COMORBIDITY.
SUGGESTING THAT THERE WAS NOT
REALLY A BIG DIFFERENCE IN
PRIORITY BEFORE INFECTION IN A
POPULATION OF PEDIATRIC PATIENTS
WHO ARE GOING TO JUST HAVE ACUTE
COVID VERSUS THOSE WHO ARE GOING
TO HAVE MISC AFTER THE FACT.
MEANWHILE HERE'S A BUNCH OF LAB
VALUES AND THRESHOLDS FOR
EXTREMISTS AND THE POPULATIONS
ARE WILDLY DIFFERENT SUGGESTING
A COMPLETELY DIFFERENT COURSE OF
DISEASE BEING EXPERIENCED BY
PEDIATRIC PATIENTS WITH ACUTE
COVID.
HOW DO WE TALK ABOUT THE DATA
AND WE TALKED ABOUT RESTRICTIONS
AND EVERYTHING WILL GET NICER
AND NICER HERE ON OUT.
ON THE RIGHT WE SAW GLYPHS WHERE
EACH BOX IS A CODE TRANS FORM.
THE CODE TRANSFORM CAN BE
WRITTEN IN PYTHON OR R OR SQL
AND IT IS INHERENTLY VERSION
CONTROLLED.
THIS WORKBOOK CAN BE ACCESSED BY
MULTIPLE USERS IN A PROJECT.
IN FACT YOU CAN HAVE ONE PERSON
WHO IS GOOD AT WRITING CODE IN R
AND IT FLOWS SEAMLESSLY TO A
TRANSFORM WHICH FLOWS SEAMLESSLY
INTO A PYTHON TRANSFORM AND THEY
WORK TOGETHER NICELY WHICH
ALLOWS FOR GOOD COLLABORATION.
AND ALSO ALLOWS FOR RETENTION OF
PROVIDENCE.
WE CAN GO FROM A FIGURE AND
TRACE BACK UP HILL WHAT STEPS
WERE PERFORMED AND WHO WROTE THE
CODE AND WHEN THE CODE WAS LAST
RUN TO CREATE THE UNDERLYING
PIECE.
YOU CAN QUICKLY DETECT IF A
PIECE OF YOUR PIPELINE IS STALE
AND NEEDS TO BE RERUN AND BUT
EVERYTHING ON SCHEDULES SO THAT
YOUR FIGURES UPDATE
AUTOMATICALLY WITH EACH NEW
WEEKLY RELEASE OF THE DATA.
THE THING THAT'S POWERFUL ABOUT
THE DATA IS IT EXISTS WITHIN THE
PLATFORM AND IN ADDITION TO THE
NICE COLLABORATIVE WORK SPACES,
EVERYTHING IS INHERENT LY
PARALLELED COMPUTED WHICH IS
NECESSARY WITH THE AMOUNT OF
DATA.
IT'S HARD TO SEE BUT THAT'S ONE
SUCH TRANSFORM AND WE'RE DOING
AGGREGATES WHEN TRANSFORMING THE
DATA AND THE PIECE OF CODE IS
TRYING TO FIND THE LONGEST VISIT
A PATIENT HAD WAS ALSO COVID
INFECTED.
IN OCTOBER OF 2021 WE JOINED THE
RECOVER EFFORT AND INVESTIGATING
PASC AND LOOKED AT WHAT IS LONG
COVID OR THE SUBTYPES AND RISK
FACTORS AND INTERVENTIONS THAT
AFFECT PASC.
IN ORDER TO DO THIS WE USED THE
UO9 DIAGNOSTIC CODE USED FOR
POST COVID CONDITION.
WE USED IT TO BUILD MACHINE
LEARNING AND IDENTIFY PATIENTS
WITH LONG COVID AND WE PUT OUT A
PAPER CHARACTERIZING THE
PATIENTS IN TERMS OF WHAT OTHER
DIAGNOSES OR MEDICATIONS OR
PROCEDURES DO THEY HAVE AROUND
THAT DIAGNOSIS.
WE'RE ABLE TO APPLY MACHINE
LEARNING TO IDENTIFY THE
PATIENTS AND APPLY IT TO OTHER
PATIENTS WHICH DON'T NECESSARY
HAVE A DIAGNOSIS AND IDENTIFY
WHAT WE THINK IS THE REAL
POPULATION OF PATIENTS WITH LONG
COVID.
MOST OF IT IS NOT SURPRISING
WHICH IS GOOD WHEN YOU EVALUATE
THE MACHINE LEARNING MODEL AND
HIGHLIGHTING IN YELLOW LOOKING
AT PLOTS.
THE RED STUFF IS ON THE RIGHT
SUGGESTING THAT THIS VARIABLE
DIFFICULTY BREATHING IS
POSITIVELY ASSOCIATED WITH PASC
OR LONG COVID AND ALTERNATIVELY
IN BLUE THE VACCINATION IS
ASSOCIATED WITH A LOWER RISK OF
PASC.
YOU CAN TAKE THE PATIENT AND
OBVIOUS NEXT STEP IS CLUSTERING
AND VARIOUS GROUPS I'LL GLOSS
OVER FOR TIME HERE.
SO MOST IMPORTANT QUESTION I'VE
TIE
TEASED THE LARGE DATA SET AND
HOW DO YOU GET ACCESS, COVID.
CD2H.org.
WE NEED AN INSTITUTIONAL DATA
USE AGREEMENT WHICH AT THIS
POINT ALL YOUR INSTITUTIONS
PROBABLY ALREADY HAVE ONE SIGNED
WITH N3C.
I'M SHOWING THE NETWORK OF
COLLABORATORS AT EMORY AND
ATLANTA AND THE NETWORK OF USERS
ARE BROADER THAN MY OWN SOCIAL
NETWORK.
I DON'T KNOW HALF THE PEOPLE AT
EMORY.
THERE'S PROBABLY USING IT
ALREADY.
YOU REGISTER WITHIN 3C AND YOU
NEED A LOCAL IRB AND YOU STILL
NEED THE DETERMINATION LETTER.
IT GOES TO THE DATA ACCESS
COMMITTEE WHICH IS NIH AND THEY
JUST CHECKED FOR APPROPRIATE USE
TO MAKE THE IRB MATCHES AND THEN
DO THE WORK WHICH I THINK IS
GETTING EASIER AND EASIER.
A LOT OF CODE IS POSTED IN WHAT
WE CALL KNOWLEDGE STORE ACROSS
PROJECT AND THERE WAS ZERO
DOLLARS OF COMPUTE COST AND
OFFERED AS A FREE RESOURCE.
THERE'S A PASSIVE LIST OF PEOPLE
I TOOK FROM THE FIRST PAPER
LOOKING UP BUT I SHOULD MENTION
AGAIN N3C IS FUNDED ALMOST
ENTIRELY BY NCATS HERE AND DATA
IS COMING TO US FROM CTSA
PROGRAMS AS WELL AS DATA MODELS
FUNDED BY PCORI AND RECENTLY
SOME OF OUR ANALYTICS HAVE BEEN
SUPPORTED BY THE RECOVER PROGRAM
LOOKING INTO LONG COVID AND PASC
SO THANK YOU.
>> WONDERFUL.
FASCINATING.
I'VE KNOWN SOMETHING ABOUT THIS
BUT THE SIZE AND SCOPE AND THE
ABILITY TO DO THIS RAPIDLY IS
QUITE IMPRESSIVE.
QUESTIONS FROM ANYBODY.
I HAD ONE I FORGOT.
THERE'S A QUESTION IN THE Q&A.
>> WHEN THERE'S A STRONG PERIOD
SUCH AS THE COVID PANDEMIC, DOES
IT TRULY UNIDENTIFY THE DATA?
FIRST THE DATA IS PRETTY MUCH IN
THE ZIP CODE IS THE ONLY
IDENTIFIER LEFT THAT THE POINT
AND IN THE DATE SHIFT DATA ONLY
A THREE-DIGIT CODE AND OB --
OBFUSCATES ALMOST EVERYTHING AND
THE DATE SHIFTING IS DONE BY 180
DAYS IN EITHER DIRECTION WHICH
HONESTLY MAKES IT DIFFICULT TO
ASK A LOT OF THE SCIENTIFIC
QUESTIONS AROUND VARIANT.
MOST PEOPLE WIND UP TRYING TO
GET ACCESS TO THE FULL DATA
SHIFT AND 180 IS A STRONG SHIFT
ESPECIALLY IF IT'S THE ONLY
LIMITED IDENTIFIER LEFT.
>> EXCELLENT.
WE HAVE ANOTHER QUESTION THE
NUMERATOR IS GREAT AND HOW DO
YOU ACCOUNT FOR MORE.
>> THE DATA IS CONTINUALLY
REFRESHED TO THE EXTENT THE
E.H.R.s ARE REFRESHED AT THE
SITE.
WE HAVE CONTINUOUS FOLLOW-UP.
>> IF MR. SMITH SHOWED UP FOR
RECOVER AT NYU LANGONE AND THEN
WENT HOME AND NEVER CAME BACK
BUT ENDED UP DYING AT MOUNT
SINAI OR AT AN OUT-PATIENT
CLINIC ON LONG ISLAND WOULD YOU
KNOW ?
>> A, THAT'S A PROBLEM.
THERE'S TWO GOOD SOLUTIONS WE
HAVE.
FIRST WE HAVE PATIENT
DUPLICATION IN A PRIVACY
PRESERVING WAY.
IF THEY'RE IN THE DUPE AND UNC
DATA SET WE SHOULD BE ABLE TO
IDENTIFY AND PUT THE PATIENT
BACK TOGETHER AGAIN.
AS FAR AS OUT OF HOSPITAL
MORTALITY THAT'S ONE WHERE THEY
SOUGHT EXTERNAL MORTALITY DATA
SPECIFICALLY AND THOSE ARE LINK
IN WITH THE SAME PRIVACY
PRESERVING LINKAGES.
>> UP NORTH CAROLINA WHAT
PERCENTAGE OF THE HOSPITALS ARE
IN YOUR SYSTEM?
>> I DON'T KNOW I CAN QUOTE THAT
NUMBER OFF THE TOP OF MY HEAD.
>> IT CAN BECOME MORE
COMPLICATED.
>> THE SAVING GRACE I THINK IS
THAT WE HAVE PRETTY BROAD
NATIONAL COVERAGE NOW AND THE
TIME SCALE WE'RE TALKING ABOUT
WITH MOST THE PEOPLE AND THE
QUESTIONS OF COVID IS RELATIVELY
SHORT.
WE'RE NOT TALKING ABOUT A
20-YEAR STUDY.
HOPEFULLY THAT'S REDUCED BUT
THIS IS A PROBLEM WITH ANY
E.H.R.-BASED STUDY.
WE CAN WRITE A TEXTBOOK AND I'M
SURE TEXTBOOKS HAVE BEEN WRITTEN
ABOUT LIMITATION WITH CONVENIENT
SAMPLES FROM E.H.R.
ONE THING WE DEFINITELY DON'T
WANT TO DO IS ASK QUESTIONS
ABOUT WHO IS MORE LIKELY TO GET
COVID.
IT'S A COMMON QUESTION PEOPLE
LOVE TO ASK AND WE SAY DON'T ASK
THAT QUESTION BECAUSE OUR DATA
SET ARE PEOPLE WOULD HAVE COVID
AND MATCH CONTROL.
AUTOMATICALLY DON'T ASKS ABOUT
SUSCEPTIBILITY.
ALL THE GOOD QUESTIONS ARE
WITHIN THE PEOPLE WITH THAT GET
COVID WHAT HAPPENS.
>> WONDERFUL PRESENTATION AND
RICH DATA AND THANK YOU VERY
MUCH AND WE'LL HAVE THE QUESTION
AND ANSWER PORTION AT THE END.
IT'S MY PLEASURE TO INTRODUCE
DR. JESSILYN DUNN AND SHE'S BEEN
CO-HOSTING AND IT'S A PLEASURE
TO GET TO KNOW HER AND MIGHT AS
WELL REPEAT HER QUALIFICATIONS.
SHE'S AN ASSISTANT PROFESSOR OF
BIOMEDICAL ENGINEER AND BIO
STATISTIC AND BIO INFORMATICS AT
THE DUKE AND DIRECTOR OF THE BIG
IDEAS LABORATORY TO PREVENT
CHRONIC DISEASE THROUGH DIGITAL
HEALTH INNOVATION AND SHE'S P.I.
OF THE COVID STUDY TO MONITOR
COVID-19 USING MOBILE HEALTH
TECHNOLOGIES AND PART OF THE AN
OPEN SOURCE PROGRAM FOR DIGITAL
BIOMARKER DEVELOPMENT.
AND HAS BEEN A POST-DOCTORAL
FELLOW SO SHE CAN TELL YOU MORE
ABOUT THE NIH TRAINING
MECHANISMS.
THANK YOU VERY MUCH, JESSILYN.
TAKE IT AWAY.
>> THANK YOU FOR CO-CHAIRING
WITH ME.
IT'S GREAT IT GIVE MORE DEEP
DIVE INFORMATION ON DATA IN
EPIDEMIOLOGY.
I WANT TO RE-EMPHASIZE WHY I
PERSONALLY AM SO EXCITED ABOUT
DIGITAL HEALTH DATA.
AND THE IDEA HERE ON THE
LEFT-HAND SIDE IS SHOWING ALL
THE DIFFERENT PHYSIO LODGE
SYSTEMS THAT WE CAN MONITOR
NON-INVASIVELY THROUGH SENSORS
THAT ARE EITHER BODY WORN OR IN
THE ENVIRONMENT.
RIGHT NOW WE THINK ABOUT SMART
WATCHES AND THINGS YOU HAVE TO
REMEMBER TO PUT ON AND WE'RE
HAVING INCREDIBLE ADVANCES IN
NOT ONLY COMPUTE BUT IN HARDWARE
AND MINIATURIZATION.
SO WE'RE MOVING FROM DEVICES TO
DEVICES THAT INTEGRATE
SEAMLESSLY WITH OUR LIVES.
FOR EXAMPLE, FABRIC IN CLOTHING.
YOU CAN SEE IN THE BOTTOM
PICTURE WE PAINTED ON
ELECTRONICS.
YOU MAY HEAR ABOUT TATTOO
ELECTRONICS BUT WHAT WE'RE
MOVING TOWARDS ARE TYPES OF
SENSORS THAT ARE HARDLY
NOTICEABLE AT ALL.
SO WHEN WE THINK OF GOING FROM A
MONITOR SAY FROM THE BULKIEST IS
IN-CLINIC ECG AT AN HOME MONITOR
TO MEASURE ECG OVER 24, 48 HOURS
THAT INVOLVED A CLUNKY PIECE OF
EQUIPMENT BUT COULD BE DONE IN
THE HOME TO TODAY PATCHES THAT
SIT ON THE CHEST AND THEY'RE
ABOUT THIS BIG AND WE CAN THINK
AHEAD TO THE FUTURE OF THESE
STICKERS OR TATTOOS THAT COULD
CONTINUOUSLY MONITOR HEART
RHYTHMS.
WHERE WE'RE TODAY IS JUST THE
BEGINNING.
WE ARE AT THE PRECIPICE OF THE
ABILITY TO MEASURE ALL THE BIO
SIGNALS PASSIVELY IN THE
BACKGROUND AND CONTINUOUSLY.
ON THE NEXT SLIDE I'LL TALK
ABOUT THE OVER ARCHING STRUCTURE
OF THE TALK.
ON THE NEXT SLIDE I'LL TALK
ABOUT WHAT A DIGITAL BIOMARKER
ACTUALLY IS SO WE'RE ALL ON THE
SAME PAGE IN TERMS OF
TERMINOLOGY.
I'LL TALK ABOUT CHALLENGES AND
THEN APPLICATION AREAS.
IF WE GO TO THE NEXT SLIDE, WHEN
I SAY THE TERM DIGITAL BIOMARKER
I WANT TO DEFINE WHAT I MEAN
IT'S DIGITALLY COLLECTED DATA
AND FROM A SMARTPHONE GPS AND
TRANSFORM THAT DATA TO AN
INDICATOR OF HEALTH OUTCOMES
LIKE DOES SOMEBODY HAVE AN
INFECTION.
YOU CAN THINK OF IT AS AKIN TO
AN ECONOMIC INDICATOR.
THINK OF GDP AN ACCUMULATION OF
MANY DIFFERENT CONTINUOUS DATA
STREAMINGS AND ANY POINT IN TIME
IT COULD BE CALCULATED TO
DETERMINE THE OVERALL HEALTH OF
THE ECONOMY.
IT'S A SIMILAR IDEA.
AND SO WITH THE MULTIMODAL
SENSING WE HAVE AND THE ABILITY
TO COMBINE THE INFORMATION WITH
OTHER TYPES OF MEASUREMENTS LIKE
BIO MOLECULAR MEASUREMENTS AND
MULTIOMICS AND MULTIHEALTH
RECORDS YOU CAN IMAGINE HOW YOU
BRING IT TOGETHER TO BRING THE
INDICATOR VARIABLES.
THE NEXT SLIDE SHOWS WHAT IT
LOOKS LIKE MATHEMATICALLY.
ESSENTIALLY WE ARE BUILDING
MODELS WITH CLINICAL
MEASUREMENTS AND ON THE LEFT
BUILDING A MATHEMATICAL FUNCTION
TO TAKE IN THE DATA.
IT COULD BE A LINEAR REGRESSION
OR AS COMPLEX AS BUILDING THAT
IS AT THE HEART OF THE BIOMARKER
DEVELOPMENT.
IF YOU GO TO THE FDA AND TRY TO
GET AN APPROVED BIOMARKER IT
WILL BE DIFFERENT LEVELS OF
EVIDENCE NEED ED TO JUSTIFY THI
IN REAL WORLD PRACTICE.
THERE'S TWO WAYS TO EXPLAIN WHAT
A DIGITAL BIOMARKER IS AND THEN
ON TO THE NEXT SLIDE I WANT TO
TALK ABOUT SOME OF THE
CHALLENGES THAT GO INTO INTO THE
DIGITAL BIOMARKERS.
THEY'RE NOT ENTIRELY DIFFERENT
FROM OTHER CHALLENGE WE HEARD
FROM OTHER PRESENTERS OVER THE
PAST TWO DAYS.
WHEN IT COMES TO VARIOUS
TECHNOLOGIES THAT GENERATE DATA.
WE WANT TO KNOW THE DATA WE'RE
GETTING IS VALID AND ACCURATE
AND OUR MEASUREMENT TOOLS ARE
DOING WHAT WE THINK THEY SHOULD
BE DOING AND WHEN WE HAVE THE
DATA AND BUILD THESE MODELS THAT
IT'S NOT OKAY TO PEOPLE WHO ARE
DOWN STREAM USERS THAT THE
MODELS ARE INTERPRETABLE.
THE DATA AND MODELS FOLLOW THESE
FAIR PRINCIPLES WHICH IS
FINDABLE, ACCESSIBLE AND
INTEROPERABLE AND REPRODUCIBLE.
SO A LOT OF THAT GETS TOWARD
THIS OPEN SOURCE SOFTWARE
COMMUNITY WE'VE BEEN BUILDING
THAT IS SPECIFICALLY CENTERED
AROUND DIGITAL BIOMARKER
DEVELOPMENT.
THEN THERE'S A WHOLE OTHER PIECE
THAT IS TOWARD THE TRANSLATION
OF THE WORK TO REAL PRACTICE.
THIS IS CHALLENGING AND I HAVE A
FEW SLIDES WHERE I WILL TALK
ABOUT SOME OF THE STICKY ISSUES
WHEN IT COMES TO REGULATORY
OVERSIGHT OF DIGITAL HEALTH
MEASUREMENT TOOLS PARTICULARLY
THOSE THAT MAY BE MORE OF
CONSUMER DEVICES RATHER THAN
DEVICES THAT ARE TYPICALLY USED
IN HEALTH CARE SETTING.
WE HAVE DATA COMING IN AND THE
TALK BY DR. MOFFITT WAS A GREAT
EXAMPLE OF TOOLS WE MAY NEED IT
RELY ON TO DO A LARGE SCALE AND
PARALLEL COMPUTE TO HANDLE LARGE
CONTINUOUS STREAMS OF DATA AND
THE LAST CHALLENGE IS THE
ETHICAL, LEGAL ISSUES AND
PRIVACY AND SECURITY AND
UNDERSTANDING THE RISK VERSUS
BENEFIT TRADE-OFF FOR
PARTICIPANTS IN THE STUDIES AND
ONE EXAMPLE OF THAT IS SOME OF
THE WORK WE'VE BEEN DOING AROUND
ABILITY TO REIDENTIFY
PARTICIPANTS FROM SIMPLE BIO
SIGNALS OR WEARABLES DATA.
ALL THESE ARE CHALLENGES THAT
ARE BEING TACKLED BY US AND
OTHER PEOPLE WORKING IN THE
DIGITAL BIOMARKER DISCOVERY
SPACE.
ON THE NEXT SLIDE I'LL TALK
ABOUT THE OPEN SOURCE SOFTWARE
COMMUNITY AND I'LL ENCOURAGE
ANYBODY WITH INTEREST IN THE
DIGITAL DATA TO CHECK OUT THE
RESOURCES HERE BECAUSE NOT ONLY
IS THIS AN OPPORTUNITY TO USE
AND SHARE DATA, CODE AND
ALGORITHMS BUT THERE'S TONS OF
EDUCATIONAL RESOURCE WE AND
OTHERS HAVE BEEN PUTTING
TOGETHER THAT GIVE AN
OVERARCHING VIEW OF DIGITAL
DATA, DIFFERENT TOOLS YOU CAN
USE TO COLLECT SUCH DIGITAL
DATA, WHAT ARE SOME OF THE
ACQUISITIONS AROUND DATA
PROCESSING AND UNDERSTANDING THE
TOOLS THEMSELVES.
SO THERE'S A LOT HERE AND WE ASK
PEOPLE TO CONSIDER PARTICIPATING
ON TWO SIDES.
ONE SIDE IS TO BE AN INGEST ER
OF THIS INFORMATION AND ON THE
OTHER SIDE IS TO BE A
CONTRIBUTOR.
THIS IS REALLY A COMMUNITY BUILT
BY US AND FOR US.
SO ANYBODY WHO WANTS TO WORK
WITH DIGITAL DATA EVEN CITIZEN
SCIENTISTS ARE WELCOME TO BECOME
A PART OF THIS.
THERE'S CONTACT INFORMATION ON
THAT WEBSITE.
CHECK OUT DBP DOT ORG AND I HAVE
MORE EXAMPLES HOW TO GET STARTED
HANDS ON.
WE SAW ONE TUTORIAL THAT WAS
DEMONSTRATING ONE OF THE SLEEP
ALGORITHMS THAT USES A SMART
WATCH AND YOU CAN USE THE QR
CODE AND SCAN IT WITH YOUR
CAMERA AND BE LED TIE HOST OF
OTHER CASE STUDIES THAT SHOW HOW
TO USE CONTINUOUS GLUCOSE
MONITO
MONITORING DATA AND MANY MODULES
AS WELL AS CODE BOOKS TO FOLLOW
LONG AND RUN SNIPPETS OF CODE TO
SEE HOW IT WORKS IN REAL TIME.
SO HOPEFULLY FOLKS HAVE A CHANCE
TO CHECK THAT OUT AS WELL.
GOING BACK TO CHALLENGES, I WANT
TO TALK ABOUT REGULATORY
CONSIDERATIONS.
THIS IS AN INTERESTING PROBLEM
WE'VE RUN INTO AS WE START TO
THINK ABOUT HOW WE EXPLAIN THE
LIABILITY OF THE DATA TO
DIFFERENT USERS.
SO A LOT OF THE QUESTIONS WE GET
COME FROM CLINICIANS OR FROM
PATIENTS EITHER USING THEIR
CONSUMER WEARABLE DEVICE IN
THEIR EVERYDAY LIFE OR BRINGING
INFORMATION FROM THOSE DEVICES
TO THEIR DOCTORS.
THE QUESTIONS WE GET ARE HOW
RELIABLE IS THE DATA AND IF I
SEE SOMETHING ABNORMAL WHAT IS
THE NEXT BEST THING TO DO.
THIS IS AN EXAMPLE THAT EXISTS
ON MULTIPLE DEVICES, APPLE
WATCH, FITBIT, CARMEN THERE'S
FUNCTIONALITIES OF THE DEVICE
REGULATED AND OTHER
FUNCTIONALITIES NOT FDA
REGULATED.
IT'S CONFUSING.
WHEN IT'S MARKETED TO CONSUMERS
THERE'S DIFFERENT LEVELS OF
CERTAINTY DEPENDING ON WHICH
FUNCTIONALITY YOU'RE LOOKING AT.
THE EXAMPLE IS THE IRREGULAR
HEART RHYTHM DETECTION ALGORITHM
FDA CLEARED ON SEVERAL DEVICES
AND THE SAME DEVICES HAVE SPO2
FUNCTIONALITY NOT CURRENTLY
REGULATED AND THE FINE PRINT
SAYS DON'T USE FOR MEDICAL
PURPOSES AND BUT THE
FUNCTIONALITY WAS RELEASED
DURING THE COVID-19 PANDEMIC.
WE HAVE TO WONDER, WHAT ARE
PEOPLE THEN USING THE SP 02
INFORMATION IF IT'S NOT TO
MONITOR HEALTH?
THE RECOMMEND IS TO USE THIS FOR
MOUNTAI
MOUNTAINEERING, FOR EXAMPLE.
AS IT IMPORTANT TO BE
CONSIDERING.
START TO UNDERSTAND HOW TO
BETTER REGULATE THE PRODUCTS AND
INFORM CONSUMERS, PATIENT AND
PROVIDERS ABOUT THE DIFFERENT
LEVELS OF EVIDENCE THAT SUPPORT
HOW TO USE THE DATA.
THE ANSWERS ARE NOT ALWAYS
CLEAR.
AGAIN, THIS SAY COMMUNITY EFFORT
TO START TO UNDERSTAND WHAT ARE
THE OPEN ISSUES AND HOW SHOULD
WE BE DRESSING THEM.
SO WITH ALL THAT BACKGROUND I
WANT TO GO TO ONE SPECIFIC
EXAMPLE OF DIGITAL BIOMEDICAL
PROJECT THAT WE'VE BEEN WORKING
ON AND THAT IS CENTERED AROUND
INFECTIOUS DISEASE DETECTION.
WE START THE WORK I'M GOING TO
TALK ABOUT ALL THE WAY BACK WHEN
I WAS DOING MY POST-DOCTORAL
WORK AT STANFORD MORE THAN FIVE
YEARS AGO AND THAT WORK HAS
CONTINUED.
WHAT WE'VE BEEN TRYING TO DO IS
UNDERSTAND HOW CHANGES IN
PHYSIOLOGY THAT OCCUR WHEN
SOMEBODY HAS AN INFECTION CAN BE
DETECTED THROUGH SIMPLE CONSUMER
SMART WATCHES.
SOME OF THE WORK WE DID PRIOR TO
THE COVID-19 PANDEMIC WAS IN
DETECTING VIRAL INFECTIONS IN
DETECTING INFLUENZA.
WE HEARD EARLIER FROM MIKE
SNYDER ABOUT THE LYME'S DISEASE
DETECTION AND DID WORK WITH H1N1
AND THEN COVID HIT.
ON THE NEXT SLIDE I WANT TO TALK
HOW WE GET BEGAN THINKING ABOUT
THE INFRASTRUCTURE FOR FUTURE
PANDEMICS AND WE WERE CLEARLY
NOT THE ONLY ONES THINKING ABOUT
THIS.
THERE WERE LOTS OF EFFORTS IN
HOW TO TAKE SOME OF OUR ANALOG
METHODS OF PANDEMIC PREVENTION
TOWARDS MORE DIGITAL EXAMPLES.
CASE INVESTIGATIONS IS A GREAT
ONE WHEN WE THINK OF CONTACT
TRACING.
THE VERY ANALOG METHOD WAS TO
CALL PEOPLE AND ASK THEM WHO
THEY HAD BEEN IN CONTACT WITH
AND PERFORM CONTACT TRACING IN
AN ANALOG WAY.
HOWEVER, WE KNOW SMARTPHONES
GIVE INFORMATION ABOUT
PROXIMITY.
THAT'S A WAY TO AUTOMATE AND
DIGITIZE THE INFORMATION SO IT
CAN BE PERFORMED ON A LARGER
SCALE.
WHAT WE HAD BEEN THINKING ABOUT
IS HOW WE COULD LEVERAGE
WEARABLES.
THAT'S OUR OF INTEREST.
YOU CAN SEE THERE'S SEVERAL
OTHER OPPORTUNITIES FOR
EPIDEMIOLOGIC SURVEILLANCE,
COMMUNICATING INFORMATION,
MONITORING THE EFFECTIVENESS OF
VACCINES.
THERE'S A LOT HERE THAT CAN BE
DONE FOR PANDEMIC PREVENTION.
ON THE NEXT SLIDE IF YOU CAN HIT
TWICE WE HAVE OPPORTUNITIES FOR
DETECTION AND MONITORING.
THERE ARE TWO AREAS OF FOCUS
WHEN WE WERE LOOKING AT
IDENTIFYING COVID, WE WERE
THINKING ABOUT WHERE THE USE OF
WEARABLES WOULD BE MOST
EFFECTIVE.
WE COULD PERFORM EARLY DETECTION
AT HOME MONITORING, PUBLIC
HEALTH MONITORING AND HOSPITAL
TRIAGE.
BUT WITH THE BACKGROUND
INFORMATION WE HAD EARLY
DETECTION IN PUBLIC HEALTH
MONITORING STOOD OUT AS TWO
POSSIBILITIES TO CHANGE THE
DIRECTION OF THE INFECTION
OUTBREAKS.
ON THE NEXT SLIDE I'LL TALK
ABOUT THE IDENTIFIED STUDY --
THE COVID ENTIFY STUDY AND WHO
MAY NEED SPECIALIZED RESOURCES
AND GOING BACK TO THE CONCEPT OF
DIG IT WILL BIOMARKERS WE CAN
LOOK AT SUSCEPTIBILITY TO
INFECTION AND WHETHER SOMEONE
HAS A CURRENT INFECTION JUST BY
LOOKING AT DATA ON SMART WATCHES
AND LETTING SOMEBODY KNOW THEY
MAY BE SICK BEFORE THEY FEEL
SYMPTOMS.
HOW CONTAGIOUS A PERSON MAY BE
AND WHERE THE TRAJECTRY OF THE
ILLNESS AND IN MARCH 2020 WE
KNEW THIS WAS AN IMPORTANT
DIRECTION TO GO AND CREATE THE
ONLINE PLATFORM TO CONSENT FROM
ANYWHERE AROUND THE COUNTRY AND
EVEN GLOBALLY PAYING ATTENTION
TO PRIVACY RESTRICTIONS AND WE
WERE ABLE TO CONSENT 7,348 AND
OF THOSE YOU CAN SEE THE NUMBERS
DWINDLED DOWN WHEN WE LOOKED AT
THE COVID CASES.
WHO ACTUALLY HAD TESTING TO GET
USEFUL INFORMATION FROM THAT.
FOR THOSE WHO HAVE TESTING SO WE
HAVE 132 COVID POSITIVE CASES
HERE YOU CAN SEE LOOKING AT JUST
SMART WATCH DATA FROM COUNTS AND
RESTING HEART RATE THERE'S A
CLEAR CHANGE IN BEHAVIOR AND
PHYSIOLOGY PRIOR TO THE TESTING.
THIS PROVIDES A WINDOW OF
OPPORTUNITY TO DETECT SOMEBODY
MAY HAVE AN INFECTION BEFORE
THEY KNOW IT.
I'LL MENTION HOW WE GOT TO APPLY
THIS IN REAL WORLD SETTINGS.
MANY EXPERIENCED THE CHALLENGE
OF GOING TO THE PHARMACY FOR A
COVID TEST AND THERE WERE NO
TESTS ON THE SHELF AND THE
INABILITY TO BEST ALLOCATE
LIMITED RESOURCES DURING THIS
TIME WHEN THERE WERE SURGES IN
DEMAND FOR COVID TESTS.
ON THE NEXT SLIDE I'LL MENTION
HOW WE THOUGHT ABOUT THIS
PROBLEM.
IF WE HAVE A FIXED NUMBER OF
TESTS AND MORE PEOPLE THAN WE
HAVE TESTS HOW DO WE BEST
DETERMINE HOW TO ALLOCATE THOSE
TESTS?
THE IDEA IS TALKING ABOUT THE
BAYESIAN APPROACH WE'VE BEEN
TALKING ABOUT THROUGHOUT THE
WORKSHOP AND WE CAN LEVERAGE A
PRIORITY INFORMATION THAT COMES
NOT ONLY FROM PEOPLE'S SYMPTOMS
AND FEELINGS OF BEING ILL BUT
ALSO FROM THEIR SMART WATCH DATA
TO GIVE MORE INFORMATION ABOUT
THEIR LIKELIHOOD OF ACTUALLY
HAVING AN INSPECTION.
THROUGH AN INTELLIGENT TESTING
ALLOCATION ALGORITHM WE CAN
INCREASE THE LIKELIHOOD WE
DETECT POSITIVE CASES FROM A
FIXED NUMBER OF TESTS.
ON THE NEXT SLIDE I'M SHOWING
HOW WE ACTUALLY GO THROUGH THIS
AND YOU CAN CLICK ONE MORE TIME
TO SHOW WE HAVE DIFFERENCES IN
THE PHYSIOLOGY AND BEHAVIORS
BETWEEN PEOPLE WHO DO AND DON'T
HAVE COVID.
YOU CAN SEE ON THE LEFT-HAND
SIDE AND BUILDING AN ALGORITHM
DISTINGUISHING BETWEEN PEOPLE
WHO HAVE COVID AND NOT WE HAVE A
LOT OF PARAMETERS TO OPTIMIZE
AND THIS IS JUST LOOKING AT
RESTING HEART RATE AND STEPS
FROM A SMART WATCH AND YOU CAN
IMAGINE HOW THIS OPTIMIZATION OF
FEATURES SCALES.
SO THERE'S A LOT OF HYPER
PARAMETER TUNING WELL DESCRIBED
IN THE PAPER CITED AT THE
BOTTOM.
OND THE NEXT SLIDE A QUICK
SHOWING OF RESULTS WE CAN SEE
THAT THIS METHOD ALONE PERFORMS
WELL IN DISTINGUISHING WHO IS
LIKELY TO HAVE A COVID INFECTION
AND NOT AND BECOMES MOST
EFFECTIVE IN PANEL C AND F WHEN
WE HAVE A SMALLER NUMBER OF
TESTS THAN PEOPLE TO TEST.
THIS IS GOING TO BE VERY USEFUL
TO IMPLEMENT AT THE BEGINNING OF
LET'S SAY A NOVEL OUTBREAK OF A
NOVEL VIRUS.
I'LL MENTION A FEW KEY RESULTS
FROM THIS.
THE INTELLIGENT TESTING
ALLOCATION RESULTS IN A 6.5 FOLD
INCREASE IN THE POSITIVE
TESTING.
YOU HAVE THE RESTING HEART RATE
GIVING DIFFERENT INFORMATION AND
HELPS TO FURTHER REFINE THE
ALGORITHMS.
ON THE NEXT SLIDE I'LL MENTION
AN EXAMPLE OF WORK FROM SCRIPPS
THAT SHOWS THE POWER ON A LARGE
SCALE.
BEING ABLE TO PREDICT INFLUENZA
OUTBREAKS USING FITBIT DATA FROM
ACROSS THE COUNTRY BEFORE THE
OUTBREAKS OCCURRED.
THE CDC HAS A TWO-WEEK LAG FROM
PUBLIC HEALTH AGENCIES AND
STATES BUT FITBIT CAN BE USED IN
REAL TIME.
WE HAVE DIGITALLY COLLECTED DATA
TRANSFORMED TO INDICATORS OF
HEALTH OUTCOMES AND TO BE ABLE
TO BUILD THAT AS A COMMUNITY WE
HAVE OPEN SOURCE PLATFORM AND
THE GOAL IS TO USE THE
BIOMARKERS AS EARLY INDICATION
OF INFECTION.
THIS IS ALL THE THANK YOUs FOR
ALL THAT CONTRIBUTED TO THE WORK
AND I'M NOT SURE THERE'S TIME
FOR QUESTIONS SO I'LL HAND IT
BACK OVER TO JOE.
>> THANK YOU VERY MUCH.
>> THAT WAS A LOVELY TALK AND
WEARABLES ARE CLEARLY EVERYWHERE
AND INCREASING.
IS WE MAY HAVE TIME FOR ONE
QUESTION.
HOW DO YOU CAPTURE DATA FROM THE
WEARABLES.
?
>> DIFFERENT DEVICES HAVE
DIFFERENT METHODS OF DATA
COLLECTION.
THAT'S ONE OF THE LARGEST
ASPECTS OF THE WORK WE DO AND
MANY TIMES IT'S USING AN API A
COMPANY HAS ACCESS TO AND BUILD
CONNECTIONS THROUGH THE
PROGRAMMING INTERFACES TO BE
ABLE TO TAP INTO THE
INFRASTRUCTURE.
FOR FITBIT, CARMEN, APPLE WATCH,
SAMSUNG, EACH HAS A DIFFERENT
API AND REQUIRE DIFFERENT
METHODS OF CONNECTION.
LOTS OF SOFTWARE DEVELOPMENT
BEHIND THE SCENES.
>> WONDERFUL.
WE'LL QUEUE UP THE NEXT SPEAKER
AND KNOW YOU'LL BE WITH US AT
THE END FOR THE Q&A.
IT'S BEEN FASCINATING.
I'VE LEARNED A LOT WHICH IS
GREAT.
I WANT TO INTRODUCE OUR NEXT
SPEAKER.
DR. TURAKHIA.
HE'S AN INTERNATIONALLY
RECOGNIZED CARDIAC
ELECTROPHYSIOLOGIST RESEARCHER
EXECUTIVE AND LEADER IN DIGITAL
AN HEART RHYTHM CARE AND THE
CHIEF MEDICAL OFFICER AND CHIEF
SCIENTIFIC OFFICER AT I RHYTHM
TECHNOLOGIES.
IT PRODUCES WHAT WE'VE USED FOR
RESEARCH GREAT GRADE DATA
COLLECTION AND THERE FOR
CLINICAL WORK.
ARE YOU WITH US?
>> I'M HERE.
CAN YOU SEE ME OKAY?
OKAY.
I BELIEVE IT'S 20 MINUTES?
I'LL TEE UP MY CLOCK.
>> PLUS A FEW FOR QUESTIONS.
>> I APPRECIATE THE CHANCE TO BE
HERE.
I THINK YOU'RE GOING TO ADVANCE
LET'S GO TO THE NEXT SLIDE.
HERE'S MY DISCLOSURES.
I'M EMPLOYED BY PUBLIC COMPANY
AND SPEAKING AS MY ROLE AS A
PROFESSOR AT STANFORD MEDICINE
AND HERE'S MY DISCLOSURES.
MY GOAL IS TO EDUCATE ON
EPIDEMIOLOGY ON SCALE AND IF YOU
LOOK AT THE DISEASE I CARE ABOUT
AND MANY ARE FAMILIAR WITH AFIB.
IT CAUSES STROKE.
FIRST YOU HAD LARGE COHORT
STUDIES.
YOU HAD THE FRAMINGHAM STROKE
AFIB WE DON'T USE AND THE
STROKES IS WHERE YOU STARTED AND
THERE WERE CLAIMS DATA AND WE
THOUGHT IT'S GREAT.
LET'S GO AT SCALE.
YOU ENDED UP WITH MODERATE TO
POORLY PERFORMING RISK SCORES
THAT WERE USED IN 1700 AND
THERE'S A PROPORTION THAT'S DOWN
WITH TIME CREATING NEW
CHALLENGES.
THAT'S WHERE WE WERE.
SO THE PROBLEM WITH ALL THE
STROKE SCORES LIKE WITH ANY
SCORES ON CLAIMS DATA THEY
DISCRIMINATE POORLY.
THE AFIB WAS DEFINED BY THE
TREATMENT BECAUSE IT WAS CLAIMS
DATA WITH A CLINICAL ENCOUNTER.
AND YOU DON'T GET THE SMALL
AMOUNTS OF AFIB AS IN MODERN
TECHNOLOGIES AND AMBULATORIES AN
WEARABLE AND CREATES CHALLENGE
IN GENERALIZABILITY.
AND THIS IS WHERE EVIDENCE IS
COMING FROM.
AND THERE'S DISTRIBUTION BUT WE
DON'T HAVE ENOUGH NUMBERS TO
HAVE CONFIDENCE INTERVALS AT
EACH SCORE AND END UP WITH A
FALSE SENSE OF PRECISION WHEN
USED IN CLINICAL PRACTICE.
AF BURDEN IS PROBABLY RELATED TO
STROKE.
THE REASON WE KNOW THIS FROM A
LOT OF DATA.
HERE'S ONE CLINICAL STUDY.
GO AHEAD AND ADVANCE.
WHAT IT SHOWED IS THAT THE
CLINICALLY DEFINED PERMANENT A
FIB HAD A GRADIENT OF STROKE AND
THE FIRST SIGNAL WE HAD.
AS YOU JUST HEARD FROM JESSILYN
THERE'S A TON OF DEVICES OUT
THERE.
I WANT TO BUILD A STORY WITH
PACE MAKERS AND IMPLANTABLE
DEVICES WE THINK ABOUT IN
WEARABLES.
THIS IS THE FIRST DATA RELATED
TO REMOTE MONITORING.
THE REMOTE MONITORING HAS BEEN
AROUND SINCE THE '90s WITH
IMPLANTABLE PACE MAKERS AND
DEFIBRILLATORS WITH REIMBU
REIMBURSEMENT AND YOU GET DATA
IN A PLACE THAT'S REVIEWED BY
CLINICIANS, IT'S REIMBURSED SO
THE WORK FLOW'S IN PLACE AND
PEOPLE LOOK AT IT.
WHAT DO YOU DO WITH THE DATA AND
CAN YOU TIE IT BACK TO E.H.R.?
SO WHAT WE DID STARTING EIGHT OR
SO YEARS AGO WAS STARTING LINK
AT THIS AND TOOK CLAIMS AND THE
DATA SETS ARE ROBUST.
IT'S NATIONAL HEALTH CARE SYSTEM
DATA BECAUSE THEY'RE ON A COMMON
DATA MODEL ACROSS THE SYSTEM
MORE OR LESS.
YOU CAN GO TO MEDICARE CLAIMS
AND DEATH FILES AND GO TO
MEDICARE DATA AND AMBULATORY
CARE AND THE NUMBERS ARE
SMALLER.
THEY'RE NOW YOU CAN ADD A ZERO
IN TERMS OF THE SAMPLE SIZE.
THIS PROVIDES A RICH OPPORTUNITY
FOR SIGNAL FOR BASELINE
PREDICTOR AND COULD BE USED IN
OUTCOME UP CERTAIN CASES.
THIS IS WHAT WE STARTED WITH.
YOUR ANSWERING DIFFERENT
QUESTIONS FROM THE SAME DATA
SETS.
THIS IS ONE OF THE FIRST STUDIES
WE DID WITH THE LINKED DATA FROM
IMPLANTABLE DEVICES TO THE V.A.
THE FIRST QUESTION IS WHAT'S THE
CARE VARIATION BEFORE WE GET TO
EPIDEMIOLOGY AND THEN GET INTO
INTERESTING QUESTIONS ON
OUTCOMES IN EPIDEMIOLOGY.
WHAT WE FOUND IS THERE WAS LARGE
VARIATION IN TREATMENT.
YOU'RE SUPPOSED TO GET
ANTICOAGULATED BRAIN FIB.
THE PROBLEM IS HOW MUCH TO
TRIGGER.
WE HAVE MORE RECENT CLARITY WITH
TRIALS PUBLISHED A MONTH OR SO
GOOD.
AGAIN, THERE'S NO LINE IN THE
SAND FOR WHAT TO DO AND IT
DIDN'T MATTER SIX MINUTES, TWO
HOURS, 24 HOURS.
THERE WAS VARIATION.
EACH BAR REPRESENTS A SITE AND
YOU CAN SEE THE SPREAD WAS FROM
SUB 5% TO 55%.
THERE'S A CARE GAP FOR SURE.
WHAT THIS ALLOWS YOU TO DO IS IT
DEMONSTRATE AN EQUIPOISE IN
TREATMENT AND LOOKING AT DATA TO
ANSWER OBSERVATIONAL QUESTIONS.
AND ONE THING USING SEVERAL
MODELS MULTIVARIABLE AND INVERSE
WEIGHTING WE FOUND THERE WAS A
CLEAR TREATMENT BENEFIT WHEN
ANTICOAGULATION WAS PERFORMED
WHAT IS MORE AM -- AMBIGUOUS
THE CONFIDENCE INTERVAL AND NOT
CLEAR WHAT TO DO AND AS YOU
STARTED SEEING VARYING DEGREES
AND THERE WERE CLEAR AND
CONSISTENT AT 24 HOURS SO AGAIN
MADE OUT MORE CONFIDENT IN
LONGER DURATION YOU WERE LIKELY
TO SEE A TREATMENT BENEFIT.
IN SHORTER DURATION WE'RE NOT
SURE AND NOT RANDOMIZED DATA BUT
WE DO THE BEST WE CAN.
THIS TRIAL WAS PUBLISHED WEEKS
AGO AND IN SOME WAYS VALIDATED
THE OBSERVATIONAL DATA NOT
ENTIRELY BUT IT FOUND WHEN YOU
START TREATING AT SIX MINUTES OR
MORE OF DEVICE DETECTED AFIB YOU
HAVE NO BENEFIT FOR THE OUTCOME
OF STROKE OR CARDIOVASCULAR
DEATH IN A COMPOSITE.
BUT THERE WAS A SAFETY SIGNAL OF
INCREASED BLEEDING.
YOU CAN'T USE AN OBSERVATIONAL
STUDY NO MATTER HOW GOOD TO BE
DEFINITIVE BUT THE SIGNAL WAS
POINTING IN A SIMILAR DIRECTION
AT A MINIMUM COULD HELP DEFINE
YOUR BAYESIAN PARAMETERS AND
ESTIMATED AFFECT SIDES.
LINKAGE IS IMPORTANT FOR MANY
REASONS.
KAISER LOOKS TO SEE IF THERE WAS
A GRADIENT OF STROKE RISK BASED
ON AFIB AND OVER ALL A RISK NOT
SURPRISE.
PATIEN
PATIENTS WERE NOT ANTICOAGULATED
AND THIS DID NOT SUGGEST THE
THRESHOLD EFFECT AND BY THE TIME
THEY WHITTLED IT DOWN SAW THE
PATIENTS.
AND WHAT THEY SAW IS EVEN WHEN
YOU STARTED ADJUSTING FOR STROKE
RISK SCORES THESE HAZARD RATIOS
DID NOT MOVE.
THE OVER ALL PROBLEM IS THAT
THOUGH TERTILES THEY WERE SMALL
BURDEN AND STARTING COMPARING
THE THIRD TERTILE VERSUS THE
COMBINED FIRST AND SECOND
SHOWING A GRADIENT OF RISK.
IF YOU LOOK AT STROKE ON THE
AMBULATORY MONITOR WHAT'S THE
TREATMENT BENEFIT NOT JUST THE
RISK OF STROKE BUT WHEN SHOULD
YOU ANTICOAGULATE AND WHEN IT
TRANSLATES TO THOSE WITHOUT
DEVICES YOU SEE CLEAR BENEFIT
WITH HAZARD RATIO TREATMENT FOR
.63 AND UNCLEAR BELOW PROBABLY
BECAUSE OF SMALLER NUMBERS AND
IF YOU LOOK AT STROKE INCIDENTS
UNADJUSTED ON THE LEFT GREATER
THAN 24 HOUR EPISODES AND LESS
THAN 24, YOU SEE WHAT HAPPENS
WITH TREATMENT THE GREATER THAN
24 STARTS NORMALIZING TO THE
LESS THAN 24.
AGAIN, THIS IS NOT RANDOMIZED
BUT IT GIVES A GOOD STARTING
POINT AND MAYBE GETS YOU TO 2A
OR 2B EVIDENCE UNTIL YOU HAVE A
TRIAL.
WE HAVE ALSO DONE TEMPORAL
ANALYSES TO HE SEE -- TO SEE
THE CAUSAL AND YOU SEE CAUSATION
BECAUSE THE RATIO WAS 5.4.
WHEN PATIENTS WERE USED AS THEIR
OWN CONTROL DURING THE AF
BRIEFLY PERIOD AND MORE NEAR
TERM TO THE PERIOD AND IT'S
WITHIN THE FIST SEVEN DAYS YOU
START SEEING A SIGNAL AND YOU
NEED SO MANY PATIENTS AND THE
CONFIDENCE INTERVALSZ --
INTERVALS AND THERE'S DEBATE
WHETHER AFIB IS A MARKER OR
DIRECT CAUSE OF STROKE OR BOTH.
THERE'S A PATTERN OF AFIB AND
YOU'RE AFIB BURDEN OVER ONE
MONTH COULD BE 50% AND IF YOU'RE
A FIB DOES THE PATTERN OR
SIGNATURE MATTER?
THIS IS HYPOTHESIS GENERATING
WORK THAT IT MIGHT.
IT'S THE IDEA OF TAKING THE DAY
LEVEL AF BURDEN AND OTHER
PARAMETERS IN A MEDICAL DEVICE
LINKING BACK TO HEALTH SYSTEM
DATA.
IMPORTANT WORK.
WHAT ABOUT NATURAL EXPERIMENTS,
COULD YOU LEARN SOMETHING HERE?
SO SOME OF YOU MIGHT HAVE LIVED
THROUGH THIS IF YOU'RE ON THE
WEST COAST.
I DID.
THE LOWER LEFT IS OUR HOUSE IN
2020.
THE SKY IS TOTALLY ORANGE
BECAUSE OF THE FIRE IN NAPA AND
THE FIRES IN OREGON ON THE TOP
RIGHT.
THIS IS A GREAT OPPORTUNITY IN
AN IMPLANTABLE DEVICE COHORT TO
UNDERSTAND HOW AIR POLLUTION CAN
AFFECT ARRHYTHMIAS AND START
APPLYING AS AN EXPOSURE TO SEE
WHAT HAPPENS.
WE LINKED IT TO AIR POLLUTANT
DATA FROM ZIP CODES AND USED
TEMPORAL AND GEOGRAPHIC CONTROLS
COMPARED TO NON-FIRE STATES.
YOU CAN SEE THE JUST THE EPA
DATA SHOWING A SIGNAL WITH THE
RISE IN AIR POLLUTANT TO DENSITY
OVER A SPECIFIC PERIOD AND WHAT
YOU SEE THORACIC IMPEDANCE AND
SHOCK TO HEART RATES AND PRE AND
POST FIRE AND WHAT IT DOES IN
THE AGGREGATE POPULATION.
YOU SEE CHANGES IN FISCAL
ACTIVITY AND HEART RATE AND SEE
EVIDENCE OF HEART FAILURE.
THE QUESTION IS WHY.
WE WERE INTERESTED TO SEE IF THE
AIR POLLUTANT PREDICTORS
AFFECTED ARRHYTHMIAS OR
OUTCOMES.
AT SCALE YOU CAN A LOT.
WE HAD 29,000 INDIVIDUALS.
IN CASE CROSSOVER ANALYSIS WE
SAW A CHANGE IN HEART RATE.
YOU CAN ARGUE THAT'S CAUSAL BUT
COULD BE MEDIATED BY THE FACT
WHEN THERE'S A FIRE AND YOU
BECOME INACTIVE.
EVEN WITH CASE CROSSOVER
ANALYSIS THERE COULD BE A
MEDIATOR IN THE MIDDLE.
WE SAW ASSOCIATION WITH PHYSICAL
ACTIVITY AND DIDN'T SEE ANYTHING
SHOCKS TREATMENT FOR VENTRICULAR
ARRHYTHMIAS AND IT WAS HARD TO
FINAL SIGNAL.
AND WE'RE DOING MORE WORK WE
HAVE TO GET MORE GRANULAR AROUND
INDIVIDUAL ZIP CODES AND
EVACUATIONS AND NOT USE THE SAME
TRIGGER DATE.
WE HAVE TO GET MORE GRANULAR AND
SHOWS THE POTENTIAL AND ON THE
FIRST LOOK WE WERE ABLE TO DO
SOMETHING QUITE RAPIDLY.
THIS SYNDROME IS RARE AND CAN
CAUSE SUDDEN DEATH AND IF YOU
GET THE COVID VACCINE WILL THAT
TRIGGER THIS?
MANY OF US FELT UNWELL AFTER A
COVID VACCINE.
YOU CAN VIEW THE SAFETY
ASSESSMENTS WITH LINKED DATA.
A STETHOSCOPE COMPANY USEDED A
CONNECTED THERMOMETER DEPLOYED
ACROSS SCHOOLS AND LOOKING AT
EPICENTERS FOR COVID AND WHEN
THE CAUSE OF FEVER IS HIGH AS
IT'S A PANDEMIC MORE COMPLICATED
NOW FOR SPECIFIC ILLNESS BUT
TALKS ABOUT THE POTENTIAL.
WHAT ABOUT PARALLEL HEALTH
SYSTEM COHORTS DOING THINGS
TOGETHER.
THIS IS A PROJECT WITH BETWEEN
ICSF STANFORD IN PART WITH THE
FDA AND THIS IS A SCALABLE
METHOD OF POSTMARKET
SURVEILLANCE.
AND WE USED A SET OF THIS WAS
MAINLY FROM THIS AND THE
LABELLING SCHEMA ARE HERE AND
THIS BASICALLY WE WERE TRYING TO
EXTRACT THIS OUT.
WE FOUND 34,000 PATIENTS WITH A
TARGET WEARABLE DEVICE AT
STANFORD.
AND NOW WHAT WE'RE DOING IS
TRYING TO LOOK AT DIAGNOSIS AND
CLINICAL OUTCOME AND WILL BE
PUBLISHING SOON.
HERE'S THE SITUATION WHERE WE
DIDN'T HAVE DIRECT LINKAGE AND
SHOW WHAT HAPPENED IN THE
PATIENT GENERATED DATA TO
TRIGGER MEDICAL USE.
SO THERE'S A LOT OF DATA OUT
THERE AND THINGS YOU CAN CONNECT
AND A LOT OF DATA AGGREGATED.
THE QUESTION CAN YOU COMBINE
MULTIPLE DATA SOURCES NOT JUST
ONE DEVICE WITH E.H.R. BUT WHAT
ABOUT MULTIPLE DEVICES AND YOU
TAKE THE IDENTIFIERS AND PUT IT
THROUGH A HASH FUNCTION AND HAVE
A TOKEN FOR THE IDENTIFIER.
WHAT YOU CAN DO THEN IS IF YOU
HAVE A SLEEP APNEA AND
AMBULATORY MONITOR OR SOME OTHER
TEST YOU CAN CONNECT IT ALL IN A
DEIDENTIFIED WAY.
THIS IS STRAIGHTFORWARD BUT NOW
ON THE ENTERPRISE SIDE THERE'S
WAYS TO DO THIS.
THERE'S A LOT OF APPLICATIONS
FOR EXPOSURES AND OUTCOMES.
IT'S BECOME IMPORTANT FOR A.I.
DEVELOPMENT.
YOU CAN UNDERSTAND THIS SUMMARY
AND CLAIMS DATA IS IS USEFUL AND
WE LOOK AT EPIDEMIOLOGY AND THE
AND A HALF EXPERIMENT IS ONE
EXAMPLE.
WE DON'T ALWAYS NEED THE DEVICE
DATA TO QUOTE, LINK, AS I'VE
SHOWN YOU AND ONE TO MANY
LINKAGES AND BEYOND SINGLE
HEALTH SYSTEM ARE REALLY
PROMISING FOR FUTURE WORK.
THANK YOU.
>> THANK YOU VERY MUCH.
I WAS IMPRESSED WITH YOUR FIRE
NATURAL AIR POLLUTION
EXPERIMENT.
THE BODY IS RESILIENT WHICH
TELLS US SOMETHING ABOUT
MONITORING TRANSIENT CHANGES IF
HORRIBLE AIR DIDN'T KILL PEOPLE
WITH HEART DISEASE OR LEAD TO A
DETECTABLE SYMBOL IT'S NOT EASY.
I WAS GOING TO ASK AT LEAST ONE
QUESTION SO YOU'RE DOING
CLINICAL GRADE MONITORING AND
THE PAPER SHOWS IF YOU HAVE 24
HOURS OF A FIB YOU GET BETTER.
IF YOU HAVE LESS, HARD TO PROVE.
WHAT'S THAT MEAN ABOUT THE TYPES
OF DEVICES WE'RE SEEING IN A
HEALTHY POPULATION AND TO BE
PROVOCATIVE WHERE ARE WE BETWEEN
ENTERTAINMENT AND WELLNESS AND
MEDICINE ON THIS STUFF?
>> GREAT QUESTION.
THE STRATEGY OF SIX-MINUTE
TRIGGERED COAGULATION DID NOT
LEAD TO PRIMARY OUTCOME.
THE OBSERVATIONAL DATA SUGGESTS
THAT 24-HOUR EPISODE BENEFITS
WHICH MAKES CLINICAL SENSE BASED
ON HOW A LOT OF THE SCORES
PROVIDE DATA.
REMEMBER AND WHAT WE HAVE OTHER
CLINICAL EVIDENCE TO SUGGEST
EARLIER INTERVENTION OF HEART
FAILURE COULD BE IMPORTANT.
AND EARLY IDENTIFICATION OF AFIB
COULD GO TO PBCs TO IDENTIFYING
LATENT RISK FACTORS FOR HEART
DISEASE.
AN MAYBE YOU GET THERE BY WAY OF
DETECTED ARRHYTHMIAS.
WHAT I THINK WE ALL HAVE TO
DECIDE AS A GROUP IS IS IS IT
SCREENING OR TECHNOLOGY
ENABLEMENT OF A FIFTH OR SIXTH
VITAL SIGN AND PULSE OX AND WE
DON'T DO ACTIVITY TRENDS AND
SCREENING AND IT SHOWS YOU WHEN
THE VO2 IS DECLINING
PRECIPITOUSLY.
YOU'RE IN THE GRAY ZONE OF AN
ECHO SYSTEM BETWEEN WELLNESS AND
HEALTH.
WHAT WE DON'T HAVE IS HARD DATA
TO SUGGEST IT SHOULD BE A
MEDICALLY PRESCRIBED STRATEGY.
THERE'S GOOD OBSERVATIONAL DATA
AND THE HEART LINE STUDY THAT
TIES APPLE WATCH NOTIFICATIONS
TO ENGAGEMENT AND STRUCTURED
CARE HAS COMPLETED ENROLLMENT
AND GETS REPORTED OUT NEXT YEAR.
>> WONDERFUL, WE'RE TIGHT ON
TIME AND WE'LL TAKE QUESTIONS
AFTER THE SESSION AND IT IS FAS
FATE AND NICE TO SEE REAL
CLINICAL TRIALS TO TRY AND SEE
THE BENEFIT OF WHERE WE THINK
THERE MAY BE BENEFIT AND IT'S
IMPRESSIVE SOME DEVICES FINDING
AT AN OLDER AGE THE MAJORITY OF
PEOPLE HAVE SOMETHING ALL SORTS
OF INTERESTING SOMETHING.
THERE'S A RANGE OF THINGS WE CAN
DO FOR PEOPLE WHICH WILL BE
INTERESTING.
>> THE THING WE HAVEN'T
ADDRESSED IS THE NON-SUSTAINED
B.U.T. FOR THE PANEL.
>> I'D LIKE TO RE-INTRODUCE RUI
ZHANG IN THE DIVISION OF
COMPUTATIONAL HEALTH SCIENCE AND
ASSOCIATE PROFESSOR IN THE
DEPARTMENT OF SURGERY AT THE
UNIVERSITY OF MINNESOTA.
A DIRECTOR OF NATURAL LANGUAGE
PROCESSING RESEARCH PROGRAM AND
SCIENTIFIC CO-DIRECTOR OF
INNOVATIVE METHODS AND SCIENCE
PROGRAM AT THE CENTER FOR
LEARNING HEALTH SYSTEM SCIENCE.
HE'S DONE A WIDE RANGE OF
RESEARCH INCLUDING ON NATURAL
LANGUAGE PROCESSING, TEXT
MINING, LITERATURE-BASED
DISCOVERY AND COMPLEMENTARY
INTEGRATED MEDICINE AND
INFORMATICS AND HAS BEEN WIDELY
COVERED IN THE PRESS.
THANK YOU VERY MUCH.
THIS IS FASCINATING THE NATURAL
LANGUAGE PROCESS.
>> THANK YOU, DR. CORESH FOR A
NICE INTRODUCTION.
THIS IS A PICTURE I ALWAYS LIKE
TO GIVE PEOPLE A HIGH OVERVIEW
ABOUT THE DIMENSIONALITY AND THE
COMPASS OF BIOMEDICAL BIG DATA
AND THE DIFFERENT ROLES PRESENT
DIFFERENT MODALITIES AS WELL AS
INCREASING THE COLUMNS THAT ARE
STRUCTURED ON THE LEFT PANEL AS
WE KNOW THE TRUCK STRUCTURE AND
THERE'S 80% OF PATIENT
INFORMATION.
YOU SEE THOSE ARE CHARTS OR
REPORTS OR EVEN SOME SOCIAL
MEDIA THAT WE DEFINITELY NEED
SOME WAY AND TOOL TO HELP US TO
BETTER UNDERSTAND AND REPRESENT
PATIENTS.
THERE'S ALL RIGHT RESEARCH IN
SOCIAL MEDIA AND THROUGH AN
ALGORITHM THE INFORMATION CAN
CONVERT TO A MORE STRUCTURAL WAY
AND GET SURVEILLANCE AND FROM
LITERATURE WE CAN BRING THEM
TOGETHER AS A DOWN TREATMENT
TEST FROM THE SOCIAL MEDIA
PATIENTS SHARE THEIR FIRST
EXPERIENCE ABOUT MEDICATION USE
WHICH IS BETTER TO UNDERSTAND.
IT CAN FORM THE HEALTH CARE
PROVIDER TO MAKE A BETTER
DECISION OR CREATE A NEW LINE OF
HYPOTHESIS TO FURTHER
INVESTIGATE.
1SZ WE SEE THE MODEL IN USE AND
LATER TRANSFORMER MODELS BECOME
POPULAR UNTIL THE EARLY 2019 TO
T2 AND T5 AND RECENTLY IN 2022
THERE'S A LARGE NUMBER OF MODELS
WITH CHAT GPT AND DIFFERENT
VERSIONS OF GPT 2.5 AND OTHER
COMPANIES LIKE GOOGLE BART AND
LLAMA FROM META-AND LATER THIS
YEAR GPT 4 COMES OUT AND IN MAY
ANOTHER WILL BE PUBLISHED AS
WELL.
OUR TOP THREE AREAS THE FIRST
PART I WANT TO TALK ABOUT IS IT
CAN HELP DEVELOP COMPUTATIONAL
PHENOTYPES IN THE CHART.
SO NLP HAS EXTRACTION AND
EXTRACT THE BIOMEDICAL CONCEPTS
FROM THE E.H.R. NOTES AND
THERE'S DIFFERENT TYPES OF
MODELS IN NLP.
IT'S SET UP DIFFERENT RULES AND
LARGER GROUPS AND HYBRID IS
BETWEEN THE RULE-BASED AND
MACHINE LEARNING MODEL.
AND MACHINE LEARNING CAN BE
TRADITIONAL MACHINE LEARNING AND
DEEP LEARNING AND THE IDEA IS TO
GIVE AN EVERY VIEW ABOUT THE
QUANTITY OF THE MODELS BECAUSE
THE MODELS AND YOU SEE RULE
BASED IN THE BEGINNING BUT WHEN
THE TIME CHANGE AND THERE'S MORE
AND MORE MACHINE AND DEEP
LEARNING.
AND THE DIFFERENT AREAS LIKE
DISEASE OR WORK FLOW AND THEY
COVER A LOT OF THOSE AREAS IN
DIFFERENT SPECIFIC WAYS.
THERE'S A BROAD BIOMEDICAL
DOMAIN AND THERE'S A TREND IN
PUB MED WHICH FOCUSSED ON ICU
THERE'S NO SPECIFIC FOCUSSED
MODEL THAT CAN'T EXTRACT CANCER
PHENOTYPES.
AND WE DEVELOPED A CANCER
SPECIFIC MEDICAL MODEL THAT CAN
AUTOMATICALLY EXTRACT EIGHT
PHENOTYPES AS SHOWN IN THE RIGHT
PANEL AND SEE THE ANNOTATION.
IT CONTINUES TO TRAIN IN THE
UNIVERSITY OF MINNESOTA BREAST
CANCER COHORTS IN THEIR
PATHOLOGY REPORTS TOTALLY
INVOLVED 1 BILLION TOKENS PER
TRAINING AND FORMING A
COLLABORATIVE CANCER PHENOTYPING
MANUALLY AN -- ANNOTATED DATA
SET AND REVIEWED BY YALE REVIEW.
THE RED ARE THE DIFFERENT KINDS
OF MODEL.
IN ADDITION WE FOUND THE
ORIGINAL MODEL SINCE SOME
SPECIFIC DOMAIN TERMS FOR
EXAMPLE HER 2 IS DISPLAYED IN
DIFFERENT TOKENS.
WHILE WE ADD ADDITIONAL SPECIFIC
TERMS IN THE DICTIONARY MAKE IT
BETTER RID OF THE ENTITIES.
WE COMPARE ALL THE THREE MODELS
TO SOME OTHER MODELS AT THAT
TIME WE FOUND MAJORITY OF THE
MODEL ACHIEVED A BETTER
PERFORMANCE COMPARED TO OTHERS
AND THIS IS ACROSS THE DIFFERENT
EIGHT PHENOTYPES.
IB ANOTHER CASE WE DEVELOPED A
DEEP LEARNING MODEL FOR DIETARY
SUPPLEMENTS.
THE GOAL IS TO IDENTIFY THOSE
ADVERSE EVENT VERSUS INDICATION
USING DIETARY SUPPLEMENTS.
AND DIETARY SUPPLEMENTS IS LESS
DOCUMENTED IN A LOT OF ADVERSE
EVENT REPORTING SYSTEMS AND IT'S
SOMETIMES TREATED AS FOOD.
IT'S NOT MEANT FOR ADVERSE EVENT
IN REPORTING.
WE FOUND E.H.R. IS A GOOD
RESOURCE TO DOCUMENT BY THE
PHYSICIAN MENTION SOME ADVERSE
EVENT THAT'S NOT REALLY SEVERE
BUT MODERATE OR MILD.
WE DEVELOPED A DIFFERENT MODEL
TO EXTRACT DIETARY SUPPLEMENTS
MENTIONED AND SOME SYMPTOMS
MENTIONED AND THEIR
RELATIONSHIP.
THIS IS NER AND R.E. DEVELOPED
HERE.
AFTER WE THAT WE APPLY THE
METHOD TO THE DIETARY
SUPPLEMENTS AND COMPARE ALL THE
RESULTS WITH EXISTING KNOWLEDGE
BASIS AND FOUND SEVERAL ARE NOT
REALLY KNOWN IN ANY OF THOSE
KNOWLEDGE BASES.
AND WE FOUND ABOUT 30% WERE
UNKNOWN AND AMONG NEGATIVE AND
THE RECORDS CAN PROVIDE
ADDITIONAL INFORMATION ABOUT
SAFETY USING THE DIETARY
SUPPLEMENTS.
WE CAN LOOK AT HEALTH
DISPARITIES AND FOUND STUDIES
FOR DISEASES AND THEY EFFECT THE
FUNCTION AND QUALITY OF LIFE AND
OUTCOMES.
AND IMPACT VARIOUS OUTCOMES AND
FOR THE RIGHT FIGURE YOU CAN SEE
THOSE DIFFERENT NLP METHODS CAN
BE USED TO EXTRACT DIFFERENT
TYPES OF SDH CATEGORY FROM
DIFFERENT TYPES.
WE ALSO DEVELOPED SOME LIFESTYLE
INCLUDING DIET AND PHYSICAL
ACTIVITY IN CLINICAL NOTES.
WE USE THE RULE-BASED ENGINE TO
CREATE A LABEL WITH SOME NOISE
THAT CAN HELP CREATE A LARGE
NUMBER OF PRELABELLED DATA WE
COULD TRAIN AND DEEP LEARNING
MODEL AND FINE TUNING A SMALL
ANNOTATED GOLD STANDARD AND THIS
CAN HELP US TO REALLY GET HIGH
PERFORMANCE COMPARED TO OUR
MACHINE LEARNING MODEL.
SO THIS IS A PERFORMANCE
EVALUATION ACROSS DIFFERENT
PHYSICAL ACTIVITY AND DIET.
THERE'S A SPECIAL ISSUE JUST
PUBLISHED I THINK A MONTH AGO IN
JAMA OF A VARIETY OF THE WORK
DONE TO EXTRACT DIFFERENT KINDS
OF SOCIAL DETERMINATES OF HEALTH
INCLUDING SUBSTANCE USE AND
LIFESTYLE AND ECONOMIC FACTORS,
RELATIONSHIP AND EVICTION STATUS
AND EMPLOYED AND LIVING
SITUATION AND THEN MANY STUDIES
USED N2C2 OPEN SOURCE CHALLENGE
DATA AND SOME USED NOW OPEN DATA
SOURCE TO DEVELOP THEIR OWN
MODELS.
I ENCOURAGE READERS TO READ
THOSE AND GET MORE INFORMATION.
WE FOUND THERE'S ONE ENGINE
MODEL USED AT THE UNIVERSITY OF
FLORIDA.
THEY PUBLISHED THREE TYPES OF
DATA FROM BASE MODEL AND MEDIUM
AND LARGE AND THEY HAVE
DEMONSTRATED THAT THE MODEL
OUTPERFORMED THE OTHER IN
DIFFERENT NLP TASKS AND THEY
ALSO PUBLISHED USING THE NYU
E.H.R. DATA TO FINE TUNE A LARGE
MODEL AND SUCCESSFUL IN FIVE
RISK PREDICTION MODEL I
ENCOURAGE YOU TO READ AS WELL.
AND THEY EVALUATE THE LARGE
MODEL VERSUS THE FINE TUNED
MODEL IN FIVE DIFFERENT AND THEY
USED BOTH ZERO SHOT AND ONE SHOT
FOR 3.5 AND 4 GPT AND VERSUS THE
OTHER AND THEY FOND THE FINE
TUNED THEY FIND IT CONTINUES TO
BE A PROMINENT CHOICE ACROSS
THOSE AND THERE'S INCONSISTENCY
AND ERRORS GENERATED BY THE
LARGE MODEL THAT STILL NEED TO
BE FURTHER INVESTIGATED BEFORE
IT CAN BE USED FOR CLINICAL AND
BIOMEDICAL APPLICATIONS.
IN THE SECOND SECTION I WANT TO
TALK ABOUT HOW NLP AND HOW WE
CAN LEVERAGE EXISTING CONSORTIUM
TO DO MODIFIED STUDIES AND
INVOLVE SEVERAL EVALUATIONS AND
LOOK AT THE CANCER BURDEN ACROSS
TWO INSTITUTIONS AND LEVERAGE.
HEALTH FRAMEWORK TO CONVERT N3C
DATA AND CONTRIBUTE TO N3C AND
FINALLY I'LL INTRODUCE THE
ODYSSEY APPROACH AND REPRESENT
THE DATA.
WE TREND AT THE UNIVERSITY OF
MINNESOTA BUT THE QUESTION IS
HOW CAN WE LEVERAGE THE
INSTITUTION TO DO THAT.
AS YOU KNOW THE DIFFERENT
INSTITUTION THERE'S A LOT SO
THIS STUDY WE ARE TRYING TO SEE
IF WE CONVERT THIS AND LEVERAGE
THAT TO A DIFFERENT INSTITUTION,
WHAT ARE THE BEST STRATEGIES TO
BETTER USE THAT.
SO WE COLLABORATED WITH MAYO
CLINIC AND COME UP WITH
DIFFERENT STRATEGIES.
ONE IS WE WRING THAT CANCER
BURDEN DIRECTLY FROM HERE AND
EVALUATE THAT IN MAYO AND
SECOND, TRAIN IN MAINO CLINIC
ANNOTATED DATA AND TO SEE HOW
THE PERFORMANCE CHANGED.
ALSO WE DID A DATA SET TO
EVALUATE THE GENERALIZABILITY.
THIS IS THE EVALUATION ACROSS
THE DIFFERENT DATA SET AND ALSO
COMPARED MACHINE LEARNING LIKE
CRF VERSUS THE CANCER BURDEN
ACROSS THREE DIFFERENT TESTING
SET IS COMBINATION OF THE HUMAN
DATA.
AND MAYO DATA AND HUMAN DATA.
THERE'S A MEASURE
REPRESENTATIVED IN THE TAKE SET.
-- REPRESENTED IN THE TRAINING
SET.
ZERO WOULD BE THE LAST AND ONE
IS SIMILAR.
THIS IS A BETTER PERFORMANCE IN
ALMOST EVERY GROUP AND PERFORMED
BETTER IN GROUP ONE AND TWO.
THEY DEVELOPED THE OPEN
FRAMEWORK TO BE USED AND
PUBLICLY AVAILABLE TO OTHER
INSTITUTIONS.
DURING THE N3C EFFORTS WE CAME
UP WITH THE WORKING GROUP AND
WE'RE TRYING TO SEE HOW WE CAN
LEVERAGE AND CONVERT INTO TABLES
AND CONTRIBUTE TO N3C DATA
ENCLAVE.
THIS IS A FRAMEWORK FROM THE
NLP.
THERE'S A PROCESS THAT CAN
EXTRACT A FILE INTO THE NOTE
TABLE AND IS A MORE RULE BASED
ENGINE AND CAN AUTOMATICALLY
CONVERT THE ENTITIES INTO THE
CONCEPT THAT REPRESENT IN OH NLP
AND SENT TO THE N C3T.
WE DID AN EVALUATION ACROSS
THREE INSTITUTION, MAYO,
MINNESOTA AND KENTUCKY.
WE DE-IDENTIFIED AND AND WE
FOUND THE AGREEMENT WAS VARIOUS
ACROSS THREE SITES.
THAT'S A DEMONSTRATION OF THE
DIFFERENT INSTITUTION HAVE
DIFFERENT VARIETY OF
REPRESENTATION THAT WITH RECORDS
AND THE ANNOTATION TEAM IS STILL
IN THE MAYO CLINIC BUT WHEN THEY
DO THE SAME TASK ACROSS
INSTITUTION YOU CAN THE I.A.A.
INTERAGENCY AGREEMENT IS
DIFFERENT AND THE PROCESS TAKES
FOUR MONTHS FROM DATA COLLECTION
AND DEIDENTIFICATION AND IRB
APPROVAL AND GOVERNANCE AND SEND
THOSE NOTES TO MAYO.
THAT MAY BE ANOTHER REASON WHY
WE FOUND IT'S DIFFERENT.
AND ALSO I THINK WE THINK THE AN
OWE TAILINGS IS NEEDED TO MAKE
SURE WE HAVE HIGH QUALITY
ANNOTATED DATA WHICH IS GOLD
STANDARD FOR FINE TUNING MODELS.
THERE'S EIGHT OF SPECIFIC
FACTORS SUCH AS TENSION THAT AND
STRUCTURE AND CAN IMPACT
ANNOTATION.
WE DID BOTH RULE BASED AND
LEARNING BASED AND WE USED BOTH
SINGLE SITE AND MODEST SITE DATA
SET.
IT OUTPERFORM THE DEEP LEARNING
METHOD AND WE CAN DEIDENTIFY
DATA AND THEY OUTPERFORM THE
SINGLE MODEL SINCE IT USES A
COMBINATION OF THE DIFFERENT
SITES.
THERE'S ANOTHER GROUP THAT WE
COME UP WITH AN APPROACH.
WE CAN USE A SINGLE OR LOCAL
DEVELOPED NLP SYSTEM BUT WHEN
YOU DOCUMENT THAT IN THE NOTE
NLP TABLE WE MAP THAT INTO THE
OHDSI TERMINOLOGY SYSTEM AND
THEN CONVERT THAT INTO THE
STANDARDIZATION BUT THERE'S A
LOT OF INFORMATION ON
UNCERTAINTY THAT ARE NOT REALLY
IN THE TABLE WE HAVE TO STORE IN
THE TERM MODIFICATION.
AND DIFFERENT INSTITUTION
LEVERAGES ARE OPEN SOURCE TO
DEMONSTRATE THE APPROACH ACROSS
DIFFERENT APPLICATIONS HERE.
I WANT TO TALK ABOUT NLP.
SINCE IT HAS INFORMATION AND
DEIDENTIFICATION IS A HARD TASK.
SO IT COULD POTENTIALLY PROVIDE
ANOTHER WAY YOU SEND A MODEL TO
EACH SITE AND THEN LEARNING SOME
PARAMETERS AND ENSURING THERE
ARE WAYS BACK AND THEN WE DID A
SIMULATION USING OPEN SOURCE
DATA AND FOUND THE SINGLE SITE
CLAIM IS YOU ONLY TRAIN THE
MODEL IN YOUR SINGLE INSTITUTION
AND MANY NLP TASKS PERFORM WITH
CENTRALIZED DATA AND MEANING YOU
PUT THEM TOGETHER AND LARGE
MODELS SEEM TO BE MORE RESILIENT
TO THE INCREASETION SCALE WITH
THE DATA SET INTO THE DIFFERENT
KINDS FROM 2 TO 10 YOU SEE ON
THE FIGURE ON THE RIGHT.
FINALLY I WANT TO TALK ABOUT NLP
FOR KNOWLEDGE DISCOVERY.
HOW CAN WE DO THE DRUG
REPURPOSING THAT WILL LEVERAGE
AND PUBLISH THE REPORTS AND
SCIENTIFIC PAPERS AND HOW CAN WE
USE NLP AND CONVERT THEM AND
FORM THE GRAPH AND LATER WE USE
THE COMPLETION METHOD TO DO THE
COMPLETION AND THIS IS COVID-19
IN THIS CASE DRUG REPURPOSING.
YOU SEE THE VISUALIZATION ON THE
CORNER YOU SEE THE HIGH
DIMENSIO
DIMENSI
DIMENSIONAL VISUALIZATION AND
THE POTENTIAL PATHWAY FOR THE
THIRD IN DRUGS CAN POTENTIALLY
TREAT COVID.
WE PROPOSE THREE CANDIDATE AND
WE HAVE OVERLAP WITH SOME
REPORTED STUDY USING DIFFERENT
DATA AND METHODS.
AND WE USED DEEP LEARNING TO
MAKE IT MORE EASILY TO COMPUTE
AND USING THE DISCOVERY PATTERN
TO FIND NOVEL DRUG AND DIETARY
SUPPLEMENT INTERACTION.
WE COMPARED WITH OTHER EXISTING
KNOWLEDGE GRAPH WHICH DOESN'T
REALLY HAVE A LOT OF OVERLAP
WITH THIS SUB EKG AND IDENTIFIED
POTENTIALLY DRUG SUPPLEMENT
INTERACTION AND EXTRACTED THE
PATHWAY FROM THE LITERATURE AND
THEN WE HAVE THE PLUSABILITY.
-- PLAUSIBILITY.
I'D LIKE TO THANK ALL THE EXTEND
FUNDING TO SUPPORT OUR RESEARCH
GROUP AND THIS IS OUR NLP GROUP
AND THE LAB.
>> WONDERFUL.
THANK YOU VERY MUCH.
IT'S FASCINATING WATCHING THE
MACHINE SPEAK AS WE DO.
I DON'T SEE MANY QUESTIONS AND
WE'RE BEHIND ON TIME SO I MAY
FOCUS ON ONE QUESTION FROM ME.
HOW DO YOU SEE MEASURES OF
ACCURACY FOR DIFFERENT
DECISIONS?
WE HEARD IN SOME OF THE EARLIER
TALKS THE ENTHUSIASM OF THE
PUBLIC OF PUTTING ONE CHILD
DIAGNOSIS INTO CHAT GPT AND THEY
GET AN ANSWER.
YET YOU HAVE A MILLION PATIENT
DIAGNOSES AND IT'S NOT SO EASY.
HOW DO YOU MEASURE ACCURACY AND
DO YOU SEE THRESHOLDS FOR
ACTION?
WHAT WOULD BE SUGGESTIVE,
PROBABLE, DEFINITIVE AND WORTHY
OF SURGERY TYPE OF STUFF.
>> THIS IS AN OPEN QUESTION FOR
THE COMMUNITY RIGHT NOW.
ONE THING IS TO PROCESS AND
ANALYZING THE LARGE SCALE DATA
WE REQUIRE REGULATORY SUPPORT TO
SEND PATIENT DATA TO CHAT GPT
OPEN A.I. WHICH IS NOT ALLOWED
IN MY INSTITUTION.
WE CAN USE FAKE PATIENT DATA AND
SET UP A THRESHOLD TO LOOK AT
CONFIDENCE FOR PATIENT SAFETY.
AND WE WANT TO LOOK AT CHAT GPT
FROM A SINGLE INSTITUTION AND
THE E.H.R. DATA IS PROBABLY
LIMITED SO WE LACK THE WHOLE
INFORMATION RECORD OF PATIENTS
FROM DIFFERENT DEFENSE OR SOME
OTHER LIFESTYLE.
IT DEPENDS THE USE CASE AND I
DON'T HAVE A DIRECT ANSWER FOR
THAT.
>> WELL, THANK YOU.
AS A USEFUL REMINDER THE MODELS
ARE DIFFERENT AND CHAT GPT
WHATEVER YOU PUT IN SPIES ON YOU
BACK SO THAT'S THE WHOLE OTHER
THING.
I'D LIKE TO INTRODUCE THE FINAL
SPEAKER BEFORE OUR Q&A SESSION.
SO PENULTIMATE.
WONDERFUL AND WE HAVE
DRDR.
DOCTOR PROFESSOR ALEX JARASH AT
NEO-4J AND A NON FOR PROFIT
ORGANIZATION AND HIS BACKGROUND
IS BIO INFORMATICS AND HIS
BACKGROUND IS PHARMA AND I.T.
AND HAS EXPERIENCE IN DATA
ENGINEERING AND COMBINED WITH
HIS DEEP KNOWLEDGE OF PHARMA AND
WILL TALK TO US ABOUT A
FASCINATING TOPIC AND HE'S
GOTTEN MULTIPLE AWARDS FOR
INNOVATIVE USE OF COMPUTING
INCLUDING IN THE KNOWLEDGE GAP
TO COMBAT WIDESPREAD DISEASE AND
DIABETES.
WELCOME AND THANK YOU FOR
SPEAKING TO US.
THANK YOU FOR TALKING ABOUT
HEALTH RECORDS.
>> THE GRAPH IS A DATA STRUCTURE
AND NOT A PLOT.
SO WHY ARE WHERE HE DOING
GRAPHS?
BASICALLY GRAPHS ARE EVERYWHERE
IN THE LIFE SIGCIENCE BUSINESS D
THE MOLECULES CAN BE PRESENTED
AS A GRAPH AND THE NODE AND
ATOMS AND THE ATOMIC BONDS
BETWEEN ATOMS.
ON THE RIGHT SIDE YOU PROBABLY
KNOW THE MOLECULAR PATHWAY
POSTER FROM RAUSCH PHARMA.
IT'S A GRAPH AND THE
RELATIONSHIPS AND ONE OF OUR
BEST EXAMPLES OF A GOOD GRAPH IS
OUR HOME HUMAN BRAIN.
THE PROBLEM IS THAT THIS
CONNECTED DATA IS VERY PROMINENT
IN OUR INDUSTRY WE HAVE
EXTREMELY HETEROGENEOUS DATA AND
UNSTRUCTURED DATA AND THE LEGACY
SYSTEMS WHAT WE COLLECT IS
RATHER A RELATION DATABASE WHICH
IS COLUMNS AND ROWS OR DOCUMENT
DATABASES WHICH STORE THE DATA
IN CONTAINERS AND DUMES.
-- DOCUMENTS.
AND THE INVENTOR OF THIS NEW
DATABASE CATEGORY AND THE GRAPH
DATA SCIENCE CATEGORY WE'RE 10
YEARS OLD AND THE MARKET LEADER.
FO
FOR SOME COMPANIES THEY'RE USING
GRAPH TECHNOLOGY OR DATA SCIENCE
THROUGHOUT THE DRUG LIFE CYCLE.
THEY'RE USING THE KNOWLEDGE
GRAPHS IN THE TARGET SPACE AND
LEAD OPTIMIZATION AND ANIMAL
MODELS AND TERMINOLOGY DATA AND
CLINICAL TRIALS IS A BIG THING,
REAL WORLD EVIDENCE AND UP TO
THE APPROVAL PART WE GOT A LOT
OF USE CASES BY MANUFACTURING OR
SUPPLY CHAIN RESILIENCE.
SO RISK MITIGATION AND ALSO FDA
ADVERSE REPORTING.
WE HAVE A LOT OF USE CASES
THROUGHOUT THE LIFE CYCLE.
SO HOW DO WE STORE DATA?
VERY POWERFUL.
WE HAVE THE NOTES REPRESENTED
HERE AS THE SPHERES AN CAN GIVE
THEM LABELS.
CALL IT A GENE OR ENZYME OR PUT
MORE LABELS AND TAGS ON THEM AND
THE PROTEIN CAN BE AN ENZYME AND
THE NODE IS CONNECTED BY
RELATIONSHIP AND FOR BOTH OF THE
DATA STRUCTURES WE CAN GIVE
TYPES AS FOR EXAMPLE THE LABEL
TYPE GENE OR RELATIONSHIP TYPE
CODES LIKE GENES AND PROTEIN ON
TOP OF THAT THE NODE LEVEL AND
RELATIONSHIP LEVEL WE CAN SCORE
ANY TYPE OF SIMPLE PROPERTY LIKE
STRINGS AND INTEGERS AND OBJECTS
AND THAT'S BASICALLY IT
CONNECTING TO BIGGER NETWORKS.
I THINK YOU'VE SEEN A LOT OF
KNOWLEDGE GRAPHS BEFORE.
IF YOU GO TO THE NEXT SLIDE I
HAVE TWO EXAMPLES WITH ME HERE.
ONE IS FROM DRUG REPURPOSING OR
TARGETED IDENTIFICATION WHICH IS
WHAT EVERYBODY'S DOING.
WE TALK ABOUT THE MOLECULAR
ENTITIES LIKE CODING AND RNA AND
PROTEINS CAN INTERACT WITH EACH
OTHER IF WE THINK ABOUT
PROTEIN-PROTEIN INTERACTIONS.
THE PROTEINS ARE SOMETIMES
TARGETS FOR DISEASES IN CLINICAL
TRIALS.
YOU GET THE POINT.
ALL THE MOLECULAR IDENTIFIERS
ARE CONNECTED IN A NETWORK
STRUCTURE SO NATIVELY.
AND THEN ON THE RIGHT SIDE YOU
SEE THE TWO TERMS LIKE ONTOLOGY
COMING FROM ONTOLOGY DATA LIKE
HUMAN PHENOTYPE ONTOLOGY OR
WHATEVER YOU HAVE OR LIKE YOUR
INTERNAL TERMINOLOGIES AND YOU
CAN DIRECTLY CONNECT THAT TO
YOUR KNOWLEDGE GRAPH TO HAVE
STANDARDIZED TERMS FOR YOUR
ENTITIES.
ON THE LEFT SIDE IS WHAT ALL THE
COMPANIES ARE DOING.
THEY'RE INTEGRATING ALL THE
KINDS OF UNSTRUCTURED DATA THAT
THEY CAN GET FROM LITERATURE
LIKE PUB MED AND BIO ARCHIVE AND
MED ARCHIVE OR IF THEY'RE
COMMERCIAL THEY'RE INTERESTED IN
DIGITIZING PATTERNS AND PATTERN
DATA TO SCREEN IF THEY GET
INFORMATION TO CONNECTED
ENTITIES.
THAT'S THE EXAMPLE FOR THE R&D
PART.
IF YOU GO TO THE NEXT SLIDE WE
HAVE ANOTHER ONE A DATA MODEL
WHICH IS IF YOU GO ONE SLIDE
BACK WE HAVE ONE FROM CLINICAL
TRIALS OR PATIENT JOURNEY WHICH
IS CLOSE TO THE STANDARD WHERE
WE HAVE THE PATIENT CENTRIC DATA
MODEL WHERE A PATIENT HAS
ENCOUNTERS AND THEY'RE CONNECTED
BY THE NEXT RELATIONSHIP.
SO YOU SEE THE KIND OF TIME
SERIES OF DATA AND AT SOME OR
ALL OF THE ENCOUNTERS YOU CAN
STORE DATA, CLINICAL CONDITIONS,
STORING, PROCEDURES, PAYERS,
HOSPITALS, PROVIDERS AND SO ON
AND SO FORTH.
THESE ARE ONLY TWO EXAMPLES OF
GRAPHS.
I THINK YOU WOULD AGREE IT'S
EXTREMELY INTUITIVE FROM A
NON-COMPUTER SCIENTIST
PERSPECTIVE OR FROM A BUSINESS
PERSPECTIVE.
THE GOOD THING IT'S THE SAME
DATA MODEL FOR THE ADMIN.
IT'S EASY TO DEVELOP
APPLICATIONS OR STORE DATA OR
RETRIEVE DATA FROM THAT.
I SEE MOST THE CUSTOMERS USE
KNOWLEDGE GRAPH TO STORE
KNOWLEDGE INTERNAL, EXTERNAL.
STARTING WITH THE EXTERNAL DATA
SOURCES AND IS THEY USE THIS TO
CONNECT OTHER EXTERNAL DATA
SOURCES AS I MENTIONED.
ON THE LEFT SIDE YOU HAVE
EXTERNAL ONTOLOGIES AND WHATEVER
YOU HAVE.
AND THEN THEY USE THIS KIND OF
OPEN DATA TO CONNECT WITH
INTERNAL DATA SETS AND COULD BE
TERMINOLOGIES MORE GRANULAR OR
REAL WORLD EVIDENCE DATA OR
SOMETHING LIKE THAT AND SPENDS A
KNOWLEDGE GRAPH AND YOU CAN
CONNECT THAT AS A COMPOUND
REGISTRATION TO OTHER HIGH
THROUGHPUT EXPERIMENTS OR
CLINICAL TRIALS.
AND SO FORTH.
NEXT SLIDE.
THE NEXT COUPLE OF MINUTES I'LL
SPEND ON THE WORK THAT I'VE DONE
IN MY FORMER POSITION AT THE
GERMAN CENTER FOR DIABETES
RESEARCH WHERE I HEADED THE
PROJECT BEFORE.
WE COMBINE THE DATA AND WE CAN
ZOOM IN HERE AND OTHERWISE THE
SLIDES WE'LL PROVIDE LATER
YOU'LL SEE FOR EXAMPLE THE GENES
ARE CONNECTED TO THEMSELVES AS
WELL AS THE PROTEIN AND THE RNA
AND THAT MEANS THAT THE GENES
HAVE SYNONYMS OR IDENTIFY FROM
SEVERAL DATABASES.
AND THIS IS WHAT WE COVERED AS
WELL AS IN THE DATABASE.
EVEN HISTORICAL OR LEGACY
IDENTIFIERS.
WE CAN JUMP FROM ONE TO THE
OTHER AND IDENTIFY AND SEE WHAT
KIND OF INFORMATION.
I SAID WE INTEGRATED ONTOLOGIES
AND TAKES ME BACK TO THE LAST
PRESENTATION HOW WE INTEGRATE
DATA AND CONNECT AT THE SAME
TIME WITH ONTOLOGIES.
WE START FROM UNSTRUCTURED DATA
AND WE CAN DO ENTITY RESOLUTION
AND ANY NLP OR ADVANCED
ALGORITHMS TO EXTRACT ENTITIES.
AND THIS IS WHERE THE FORMS ARE
AND YOU HAVE THE ONTOLOGY OR
STRUCTURE WHICH IS ALSO A GRAPH.
AND YOU CAN CONNECT THEM BY
MATCHING THESE STRINGS OR CODES
TO CONNECT THE ONTOLOGY TO THE
DATA.
AND WHAT YOU CAN THEN DO IS THIS
KIND OF YELLOW LINE.
YOU CAN WIRE THE ONTOLOGY AND
DOCUMENT AND CONNECT BY THE
ONTOLOGY AND THAT WORKS WELL
BECAUSE YOU CAN PUT DIFFERENT
VERSIONS OF THE ONTOLOGY OR
DIFFERENT ONTOLOGIES NEXT TO
EACH OTHER AND INFER NEW
RELATIONSHIPS BASED ON JUMPING
FROM TO THE OTHER ONTOLOGY.
I'LL GIVE YOU ONE EXAMPLE.
ON THE LEFT SIDE WE HAVE GWAS
DATA AND INTERNAL DATA FROM TYPE
2 DIABETES STUDIES LINKED TO
PROTEINS AND FUNCTIONS AND GENES
AND WE HAVE ANIMAL MODEL DATA
FROM OUR GERMAN MOUSE CLINIC
WHERE WE HAVE GENES CONNECTED TO
PROTEINS AND THEY'RE CONNECTED
TO FUNCTIONS OR TO PHENOTYPES.
THE PROBLEM IS THAT SOMETIMES
WE'RE LAGGING ON THE ONTOLOGY OR
PHENOTYPE LEVEL WE'RE LACKING
THE CONNECTION THERE.
AND WHAT WE CAN DO WITH GRAPHS
EASILY WE CAN INFER DATA AND
A -- CAN PREDICT THIS AND THERE
ARE ORTHOLOGS AND WE INFER A NEW
RELATIONSHIP BETWEEN THE
PHENOTYPE TERM IN THE DATABASE
MARK IT'S PREDICTIVE IN SILICO.
IN THE END YOU CAN MAP FROM
HUMAN TO ANIMAL PHENOTYPES AND
IT TRANSLATES AND WE LOOK IN THE
MOUSE MODEL HOW IT LOOK AND THE
UNDERLYING THINGS.
WHERE HE USE THE KNOWLEDGE GRAPH
FOR DIFFERENT KINDS OF USES.
I INTRODUCE THREE USES WE MAINLY
HAD AND SHOW YOU A LITTLE BIT
WHAT WE'VE DONE WITH THE USERS.
THE FIRST USER WAS
STRAIGHTFORWARD WHICH WAS BIO
CHEMIST DOING OMICS EXPERIMENT
MAINLY TRANSCRIPTOMICS AND THEY
WERE INTERESTED IN WHAT ARE THE
CONNECTED MESH TERMS AND
PHENOTYPES IN PUB MED AND THE
PUBLICATIONS BASED ON MY GENES
FROM MY CHIP'S DATA CONNECTED
AND WHAT THEY COULD DO IS DRAG
AND DROP AND HIT THE BUTTON AND
AUTOMATICALLY THE GRAPH WAS
QUERIED AND THE OUTPUT WAS THE
LIST OF GENES MENTIONED IN
SPECIFIC PUBLICATIONS BUT ALSO
WITH MESH TERMS OR WITH
PHENOTYPES.
VERY STRAIGHTFORWARD.
ONE INPUT AND ONE OUTPUT AND THE
APPLICATION IS SUPER EASY.
WHAT ABOUT THE USERS THAT DO NOT
KNOW WHERE TO START IN THE HUGE
KNOWLEDGE GRAPH AND DON'T KNOW
WHERE TO PROGRESS AND USE IT AS
AN INVESTIGATIVE JOURNEY THERE?
THIS GRAPH HAD ALMOST 5 BILLION
RELATIONSHIPS AND THINK OF
TRAVERSING THAT RICH DATA IN AN
EASY MANNER AND VISUALLY
EXPLORING AND FOLLOWING THE
PATHS THROUGH THE DIFFERENT
KINDS OF DATA SETS HERE.
THIS IS WHERE WE CONNECTED THE
APPLICATION.
THIS IS A SUPER NICE TOOL.
I CAN GIVE A LIVE DEMO AND IF
YOU WANT TO SEE SOMETHING LIVE
JUST LET ME KNOW AND DROP ME AN
E-MAIL.
I'M HAPPY TO HELP YOU OUT.
THE THIRD USER GROUP WAS RATHER
DATA SCIENTISTS.
THEY KNOW JAVA IS A PROGRAMMING
LANGUAGE.
THEY KNOW QUERY LANGUAGES AND
FOR THEM WE HAD I BROWSER TO DO
AGGREGATION AND WORK WITH THE
DATA AND SO THESE ARE THE USES
WE HAD AND KNOW I SHOW YOU SOME
USE CASES.
SOME WERE INTERESTED IN A GENE
AND THEY WERE INTERESTED IN THE
GENE OR PROTEIN-PROTEIN
INTERACTION FROM GENE TO RNA
INTERACTION AND THE
PROTEIN-PROTEIN ACTION AND
PROTEIN-PROTEN INTERACTIONS OF
THE INTERACTIONS AND THE RICH
NETWORK.
WE HAVE THEM AS A SAID MAPPING
THE IDENTIFIERS AND THE
DIFFERENT PUBLIC DATABASES LIKE
NCBI AND SOMETHING LIKE THAT AND
FOR THIS EXAMPLE WE WERE
INTERESTED IN THE TCF7L2 AND YOU
SEE THE ROUGHLY 15 NODE THAT ARE
BASICALLY THE SAME GENE BUT HAVE
DIFFERENT IDENTIFIERS AND WOULD
YOU KNOW THIS IS TCF7L2?
RATHER NOT, RIGHT.
AND YOU CAN EASILY QUERY OVER
THE DATABASES EASILY.
AND FOR DATABASE DEVELOPER SOME
MAY BE DATABASE DEVELOPERS IN
THE AUDIENCE, THIS IS A ONE-LINE
QUERY WHILE THIS WOULD BE AND
THEN YOU GO TO THE NEXT SLIDE WE
DID A SIMILAR EXAMPLE ON THE
RIGHT SIDE ONE OR TWO ONE LINE
OR TWO LINE STATEMENT FOR GRAPH
DATABASE FOR A PROTEIN-PROTEIN
INTERACTION AND ON THE LEFT SIDE
THE CLASSIC WAY OF DATABASES.
IT'S NOT ONLY LONG IN THE CODE
BUT ALSO VERY UNPERFORMATIVE AND
IN NATIVE GRAPH DATABASE IS
POINTERS AN MEMORY AND WE'RE WAY
FASTER THAN NORMAL DATABASES FOR
SUCH CONNECTED PROBLEMS.
IF WE THINK WE DON'T WANT TO
EXPLORE ONLY GENES AND THEIR
SYNONYMS BUT WANT TO EXPLORE RNA
AND PROTEINS AND LEGACY
IDENTIFIERS AND GENE ONTOLOGY
TERMS AND ALSO SNIPS FROM GWAS
STUDIES AND TRAITS AND SO ON.
USUALLY THE USERS ARE JUMP FROM
ONE TO THE OTHER DATABASE USING
EXCEL SHEETS AND LISTS.
THIS WON'T HAPPEN HERE.
THIS IS EVERYTHING IN ONE
DATABASE AND YOU CAN EASILY
WRITE A QUERY LIKE A FIVE-LINE
STATEMENT AND JUNG FROM THE
TRAIT DIABETES MELLITUS AND IT'S
CONNECTED TO THE GENE AND WE
JUMPED FROM REF SEQ AND SHOWED
THE RNAs AND WE JUMPED TO THE
RNA LEVEL AND THE IDENTIFIER AND
IT'S CONNECTED TO THE GENE
ONTOLOGY AND WHY ARE WHERE HE
DOING THE GRAPH?
WE CONNECTED THE DATA WITH
CLINICAL DATA WITH PATIENTS
COMING FROM ECRFs.
WE CONNECTED THE DATA FROM THE
PATIENTS.
GOING TO THE NEXT SLIDE AND WE
CONNECTED TRANSCRIPTOMICS DATA
OF THE PATIENTS.
WE HAVE DEEP FINGERPRINT OF THE
PATIENTS.
VERY RICH DATA BUT HIGHLY
UNSTRUCTURED DATA.
WE CAN USE GRAPH DATA SIGNS THAT
TAKES INTO ACCOUNT THE
STRUCTURE.
THE PTOPOLOGY TO DO THE GRAPH AD
REPRESENT THE RICH DATA IN A
VECTOR FOR MACHINE LEARNING.
I CAN GO DEEPER AND PLEASE LET
ME KNOW IF YOU NEED MORE
INFORMATION ON THAT.
AND WE CAN NOW CONNECT THE
PATIENT DATA TO THE KNOWLEDGE
GAP I SPEAK BEFORE AND ASK THE
QUESTION FROM THE LEFT TO THE
RIGHT WE HAVE AN RNA HERE A
TRANSCRIPT SUPER INTERESTING AND
WHAT DO WE KNOW FROM THE
EXTERNAL DATA SOURCES IN PUB MED
OR IN ARCHIVE.
AND WITH THE LARGE LANGUAGE
MODELS AND CHAT GPT ARE NOT THAT
GOOD BECAUSE THEY HALLUCINATE
AND IT'S A BLACK BOX AND WE GET
SOMETHING BACK WE DON'T KNOW
WHAT IT'S CAUSING AND CAN'T
EXPLAIN IT AND KNOWLEDGE GRAPHS
AND WE CONNECT THE LARGE
LANGUAGE MODEL A KNOWLEDGE GRAPH
AND WE CAN QUERY THE DATA AND
USE NATURAL LANGUAGE AND WE CALL
THIS ROUNDING.
THEY'RE CONNECTED AND IT'S AN
EXPLAINABLE WAY TO FIND THE
ANSWER OF YOUR QUESTION.
YOU HAVE GRAPH STRUCTURES IN
YOUR KNOWLEDGE GRAPH HERE AND IF
YOU GO TO THE NEXT SLIDE YOU'RE
LOOKING FOR PATTERNS YOU WANT TO
IDENTIFY FOR EXAMPLE HERE FIVE
NODES JUST LIKE A RANDOM EXAMPLE
IN THE DATA SET THAT IS KIND OF
YOUR ANALYSIS OF MAYBE A PATIENT
JOURNEY THAT IS INTERESTING OR
THIS IS AWKWARD BEING AN OUTLIER
AND NOW FIVE PATIENTS WITH THE
SAME PATTERN AND YOU LOOK FOR
PATTERNS AND PROPERTIES AND YOU
DIG DEEPER AND TRY TO EXPLAIN
WHAT IT'S ALL ABOUT.
WITH THIS I'M AT THE LAST SLIDE
OF MY PRESENTATION.
LEFT SIDE IS THE DATABASE ITSELF
HOLDING NODES AND RELATIONSHIPS
ON TOP OF THE GRAPH ALGORITHMS
AND NEW TARGETS FOR NEW DISEASES
AND WE HAVE ALGORITHMS TO FIND
BOTTLENECKS OR ALTERNATIVE.
WE CAN USE PATIENT HETEROGENEITY
FOR CLASSIFYING PATIENT AND SO
ON AND SO FORTH.
AND THE REALLY LAST SLIDE I WANT
TO SUM UP WE HAVE NOW MORE THAN
70 ALGORITHMS HELPING YOU TO
MAKE MORE OUT OF YOUR DATA
KEEPING THE RELATIONSHIPS AND
THE CONNECTIONS BETWEEN YOUR
DATA AND MAKE MORE OF YOUR DATA.
WITH THIS I HOPE I'M STILL ON
TIME AND HAPPY TO TAKE QUESTIONS
AND THANK YOU FOR YOUR ATTENTION
AND I HOPE THAT WAS INFORMATIVE
AND A DIDN'T RUSH TOO MUCH.
>> WOW.
THAT WAS A WONDERFUL WAY TO HAVE
A CONCLUDING TALK.
REALLY PROVOCATIVE AND
THOUGHTFUL.
I'LL HAVE TO HAVE YOU VISIT ME
TO THINK OF HOW TO DO SOMETHING
WITH WHAT SOUNDS LIKE BOTH
INNOVATIVE AND POWERFUL AND YET
ABSTRACT.
>> ABSOLUTELY.
AND SORRY TO BE SO ENTHUSIASTIC.
I WAS A CUSTOMER SIX YEAR AND SO
ENTHUSIASTIC ABOUT THE
TECHNOLOGY BECAUSE I HAD SO MANY
I HAD IN MY CAREER BEFORE AND IN
THE PHARMA INDUSTRY I WAS STUCK
IN THE RIGID TABLES AND THEY
WERE SPARSE BECAUSE THE DATA IS
HETEROGENOUS AND YOU CAN'T STORE
IT SO EASILY AND THE GRAPH THING
AND I WAS SUPER SURPRISED.
DO YOU KNOW THE MOST POPULAR
EXAMPLE FOR THE GRAPH DATABASE?
>> THE MOST POPULAR EXAMPLE?
>> THE MOST POPULAR IN HOW DO
YOU SAY IN THE INTERNET IS THE
PANAMA PAPERS WERECYTERR -- WER
CIPHERE D AND THE PERFORMS AND
MAKING IMPLICIT RELATIONSHIPS
BETWEEN THE ENTITIES NOW
EXPLICIT IN YOUR DATABASE IS THE
GAME CHANGER AND THE PERFORMANCE
ON A LARGE SCALE.
>> IT MAKES SENSE.
TO ME IT'S NICE TO END ON TOOLS
AND FOR ME THE KEY IS WHICH
TOOLS ARE FIT FOR WHAT PURPOSE?
I THINK WE MAY BE TOWARDS THE
END SO WE MAY BE READY TO WRAP
UP.
IF PEOPLE TURN ON THEIR CAMERAS
I'D LOVE IT TO SEE THE GROUP.
>> GRAPH IS A STRUCTURE TO STORE
DATA AND HAVE A MEANING BETWEEN
THE ENTITIES.
A CORRELATION NETWORK IS A GRAPH
AND A CORRELATION NETWORK I
THINK YOU MEAN SOMETHING LIKE
CORRELATING TO EACH OTHER.
I WOULD SAY IT'S A GRAPH.
IT'S A GENERIC GRAPH WITH ONE
TYPE OF LINK AND ONLY NUMBERS
FOR THE LINK WHEREAS IT SEEMS
YOU ALLOW FOR DIFFERENT LINK AND
CLASSES OF OBJECTS.
>> YEAH.
I OFTEN WHY AREN'T I DOING
SOMETHING IN X BUT IF YOU WANT
TO INTEGRATE BILLIONS OF NODE
AND RELATIONSHIP AND PERSIST
THAT THIS IS WHY YOU USE A GRAPH
DATABASE.
IF YOU HAVE SMALLER NETWORKS
LIKE 1,000 NODES AND SEVERAL
THOUSANDS RELATIONSHIPS, USE
WITH YOUR R APPLICATION BUT THE
BIG CUSTOMERS OF OURS AND WE
HAVE 75% OF THE FORTUNE 100 FROM
THE DIFFERENT INDUSTRIES THEY'RE
ALL USING THIS AND THE MEMORY IS
MASSIVE AND YOU HAVE TO HAVE
INTELLIGENT ALGORITHMS.
>> IF WE CAN REMOVE THE SLIDES
BECAUSE I'D LIKE TO SEE THE AL-
ALGORITHMS.
ARE THERE MULTIPLE STUDIES AND
THERE'S CONCERN THEY MAY LIVE IN
DIFFERENT PLACES, CAN WE HAVE
YOUR GEOGRAPHY LEARN FROM EACH
STUDY AND INTEGRATE IN SOME WAY
WITHOUT HAVING THE DATA MOVE?
>> ABSOLUTELY.
AND I DIDN'T WANT TO BE TOO
TECHNICAL.
I CAN'T SPEAK FOR COMPETITORS
THEY DO THEIR OWN WAY WE HAVE A
FEDERATED VERSION SO YOU CAN
SPREAD THAT EVEN IF WE THINK OF
GDPR IT MAKES SENSE TO SCATTER
IT SOMEWHERE ELSE AND IF THINK
OF FINANCIAL DATA AND COMMERCIAL
DATA THIS IS ALSO A USE CASE
THERE.
SO YES AND WE CAN HAVE HYBRID
SOLUTIONS SAYING WE HAVE A
RELATIONSHIP ON YOUR CLUSTER AND
HAVE A HYBRID SOLUTION WHERE
NON-SENSITIVE DATA ARE NOT SO
MUCH RELEVANT DATA IS SITTING IN
ANY CLOUD.
>> THERE'S ANOTHER QUESTION
ABOUT CAN WE CHECK FOR CAUSAL
RELATIONSHIPS USING THE DATABASE
TECHNOLOGY.
>> ABSOLUTELY.
AND WE KNOW THE DATA IS HIGH
QUALITY DATA.
THE SHORT ANSWER IS GARBAGE IN,
GARBAGE OUT AND IF YOU HAVE HIGH
QUALITY DATA AND DO CAUSAL
RELATIONSHIPS AND WE HAVE ONE
EXAMPLE THEY HAVE CAUSAL
RELATIONSHIPS BUT ALSO
CORRELATIONS BETWEEN SO TWO
DIFFERENT RELATIONSHIPS BETWEEN
TWO NODES AND YOU KNOW YOU CAN
SAY BETWEEN THESE TWO I WANT
RELATIONSHIP THAT'S A CAUSAL
RELATIONSHIP OR A CORRELATION.
SO YES.
>> I LIKE I GUESS THERE WAS A
MODIFICATION YOU'RE SAYING IF
YOU PUT CAUSAL RELATIONSHIP YOU
CAN GET CAUSAL RELATIONSHIPS.
IF YOU PUT IN ASSOCIATION YOU
BETTER THINK ABOUT ALL THE
THINGS CALLED SCIENCE BEFORE YOU
CONCLUDE CAUSE.
>> EXACTLY.
AND IT HIGHLY DEPENDS WHAT TYPE
OF DATA YOU'RE INTEGRATING IF
YOU'RE INTEGRATING CAUSAL DATA
BECAUSE YOU KNOW THE DATA IS
CONNECTED LIKE THES, WELL, YOU
HAVE YOUR CAUSALITY.
>> EXCELLENT.
THERE'S TECHNICAL QUESTIONS
ABOUT GRAPHING.
I DON'T KNOW THERE.
MORE AND MORE PEOPLE I LOVE
HAVING PEOPLE COME ON.
WE STILL HAVE 83 PEOPLE.
I THINK WE'RE READY FOR CLOSING
REMARKS BY PROFESSOR DUNN AND
MYSELF BUT I SEE ERIC BOERWINKLE
AS A DEAN I SEE YOU'RE TAKING
THE TIME TO ATTEND MULTIPLE
SESSIONS.
I ALWAYS LEARN HAVING LOOKED AT
THIS FOR A WHILE.
>> FIRST I'LL SAY THIS IS MORE
INTERESTING THAN BEING A DEAN.
CLEARLY, MY IMPRESSION IS THIS
SHOULD BE CONSIDERED THE FIRST
OF MULTIPLE SESSIONS LIKE IT.
THIS IS CLEARLY AN EMERGING
FIELD AND IN SOME WAYS IT'S A
SUCCESSFUL MEANING IDENTIFYING
CHALLENGES AND SOLUTIONS.
AND SO I THINK YOU ALL SHOULD
CONSIDER THIS A VERY SUCCESSFUL
MEETING.
>> THANK YOU VERY MUCH.
I'LL CALL ON GABRIEL FOR A
MOMENT BECAUSE HE CALLED ME A
FEW MONTHS AGO AND I WAS LIKE
OH, MY GOD ONE MORE THING TO DO.
BUT YOU DID A WONDERFUL BEING
WONDERFUL JOB OF DOING SO MUCH
OF THE HEAVY LIFTING THAT I KNOW
JESSILYN AND MYSELF ARE MOST
GRATEFUL AND I REALLY DO THINK
WE HAD A LOVELY MEETING SPANNING
REALLY A VERY WIDE ARRAY OF
SCIENCE.
ANY WORDS, GABRIEL?
>> NOPE.
THANK YOU, EVERYONE SO MUCH FOR
PARTICIPATING IN THE WORKSHOP.
WE DID A LOT OF WORK TO TRY TO
BRING LAKE DR. KORE CAN --
DR. CORESH MENTIONED CAST A WIDE
NET AND SCIENCE IS SO BROAD AND
WE'RE WORK IN OUR SPECIFIC SWIM
LANES SOMETIMES BUT ALL THE DATA
WE ARE TRYING TO BRING IT
TOGETHER AND I GET TOO MUCH
INFORMATION AND WE HAVE SO MANY
MISSING DIAGNOSES AND ISSUES.
WARE TRYING TO INTEGRATE ALL THE
DIFFERENT DATA SOURCES TO
ULTIMATELY PROVIDE A BENEFIT TO
THE POPULATION.
THAT'S THE BIG PICTURE AND I
APPRECIATE EVERYONE'S
PARTICIPATION AND HOPEFULLY LIKE
DR. BOERWINKLE SAID HOPEFULLY
THE FIRST OF MANY.
>> THANK YOU.
PROFESSOR DUNN.
>> I WANT TO ECHO THE THANK YOU
TO GABE, GINA, THE NIH TEAM FOR
MAKING THIS WORKSHOP HAPPEN.
I THINK ONE OF THE THINGS I
FOUND SO COOL ABOUT THE
STRUCTURE WAS THAT WE HAVE
EXAMPLES OF LARGE DATA SETS.
WE HAVE EXAMPLES OF HANDS-ON
TUTORIALS AND METHODS AND THE
IDEA IS TO TRY AND SYNTHESIS ALL
THE PIECES TOGETHER SO WE'VE HAD
A LOT OF SPECIFIC QUESTIONS WELL
IF I WANTED TO BE ABLE TO ACCESS
DATA.
IF I WANTED TO APPLY A NEW
METHOD HOW CAN I DO THAT?
THE HOPE IS WITH THE VARIETY OF
RESOURCES AND VARIETY OF
CONTACTS THAT WE'VE INCLUDED
TODAY PEOPLE START TO CONNECT
AND HOPEFULLY CONNECT THE DOTS
SO THAT WE'RE ADVANCING AT A
MUCH FASTER PACE.
AGAINST THE CONTACT INFORMATION
IS AVAILABLE FOR ANYONE OF THE
SPEAKERS.
IF YOU HAVE ANY QUESTIONS ABOUT
HOW TO GET IN TOUCH, PLEASE
DON'T HESITATE TO REACH OUT TO
THE ORGANIZERS, CHAIRS.
WE HOPE THIS IS A CATALYST TO
START OFF MORE CROSS POLL NATION
BETWEEN THE METHODOLOGISTS AND
THE DATA GENERATORS -- CROSS
POLLINATION.
>> I MAY TAKE THE PREROGATIVE OF
CLOSING.
SO LET'S TRY A MINUTE OF
SUMMARIZING THE DATA.
TODAY WE STARTED WITH THE DATA
THEMSELVES AND IT'S HUMBLING AND
AMAZING TO LOOK AT WHAT U.K. BIO
BANK, ALL OF US AND MVP ARE
DOING.
I THINK IT'S IMPORTANT TO DO
THINGS BIG, MEDIUM AND SMALL AND
CONNECT THEM.
WE HAD A CHARGED TALK BY BRUCE
PSATY AND WHEN YOU EMPHASIZE THE
SCIENTISTS AND NOT JUST THE DATA
AND REALIZE THERE'S PEOPLE
BEHIND IT IT GROWS AND THRIVES.
WE HAVE TO MAKE SURE WE DON'T
UNDER VALUE THE PEOPLE IN
THINKING ABOUT THE DATA.
THEN WE WENT INTO THE AFTERNOON
WITH NEW TOOLS, ERIC HAD WORDS
OF WISDOM WHICH I THOUGHT WERE
GREAT AND THEN IT WAS
INTERESTING TO SEE ALL THE
DIFFERENT TOOLS AND THE
MIND-BOGGLING GRAPH AND THIS
GETS ME TO THE POINT THAT I
THINK WE'RE CREATING A NEW
LANGUAGE.
SO TO SOME EXTENT MY FAMILY IS
MORE INTO HUMANITIES.
MY WIFE DREW THIS PAINTING WHICH
REPRESENTS THE SCIENCE THE HEAT
MAP FROM LOW GRF TO HIGH RISK
AND IT'S INTERESTING TO SEE
BROAD THINKERS AND I'M LOOKING
AT ERIC.
OFTEN THERE'S DETAILED STOP AND
THEN THERE'S THE BIGGER PICTURE
THAT GIVES CONTEXT.
IT USED TO BE PEOPLE NEEDED TO
LEARN A FOREIGN LANGUAGE LIKE IF
YOU LEARN ENGLISH YOU SHOULD
LEARN SPANISH OR FRENCH.
I THINK COMPUTER SCIENCE SAY
LANGUAGE AND WAY OF THINKING
THAT NOW SHOULD BECOME MANDATORY
FOR ALL OF US.
MACHINE LEARNING AND A.I. IS SO
IMPORTANT AND NOT FORGOTTEN THE
DISCIPLINES OF MEDICINE,
SOCIOLOGY, ANTHROPOLOGY AND
PUBLIC HEALTH ALL OF SCIENCE IS
TOGETHER.
I'LL CONCLUDE WITH A COUPLE
PROPOSALS I LOOKED UP ABOUT
SCIENCE.
THERE'S ONLY FOUR, WELL, FIVE
ONE IS TWO SENTENCES.
THE FIRST ONE IS SCIENCE IS THE
GREAT ANECDOTE SO THE POISON OF
ENTHUSIASM AND SUPERSTITION.
I THINK ENTHUSIASM'S GREAT BUT
APPLIES IN SCIENCE.
AND THEN BY A SCIENTIST I READ
ABOUT AS A KID MARIE COURI,
NOTHING IN LIFE IS TO BE FEARED
BUT ONLY UNDERSTAND AND WE HAVE
TO THINK ABOUT THAT AND THIS WAS
BACK WHEN RADIATION WAS NEW I
THINK HUMANITY WOULD DRAW MORE
GOOD THAN EVIL FROM NEW
DISCOVERIES.
WE HAVE TO BE HOSPIOPTIMISTIC AE
GOOD THING ABOUT SCIENCE WHETHER
OR NOT YOU BELIEVE IT IS THE
LAST QUOTE.
AT LATEST I GOT ERIC AMUSED.
THANK YOU EVERYONE FOR ATTENDING
AND UNLESS I MADE A MISTAKE,
THIS IS THE END OF OUR TWO DAYS.