WELL, GOOD AFTERNOON, EVERYONE.
WELCOME TO THE ANNUAL ADA
LOVELACE LECTURE THAT'S
SPONSORED BY THE INTRAMURAL
RESEARCH PROGRAM OF THE NATIONAL
LIBRARY OF MEDICINE.
I'M THE SCIENTIFIC DIRECTOR,
WITH APPROACHES TO INFORMATICS
PROGRAMS IN HUMAN HEALTH.
THIS CELEBRATES THE ACHIEVEMENTS
IN WOMEN IN SCIENCE, TECHNOLOGY,
ENGINEERING AND MATHEMATICS.
IT AIMS TO INCREASE THE
PARTICIPATION OF WOMEN WITHIN
THE STEM SECTORS AND SUPPORTS
WOMEN ALREADY WORKING IN STEM.
YOU MAY NOT KNOW THIS, BUT ADA
LOVELACE WAS AN ENGLISH
MATHEMATICIAN AND WRITER.
SHE WAS WORKING ON CHARLES
BABBAGE'S COMPUTER, THE FIRST
PERSON TO RECOGNIZE THE MACHINE
HAD APPLICATION BEYOND PURE
CALCULATION AND PUBLISHED THE
FIRST ALGORITHM INTENDED TO BE
CARRIED OUT BY SUCH A MACHINE.
AND SO AS A RESULT SHE'S
REGARDED AS ACTUALLY THE FIRST
COMPUTER PROGRAMMER.
TODAY'S TALK WILL BE BROADCAST
LIVING AND ARCHIVED THROUGH THE
NIH VIDEOCAST WEBSITE.
IF YOU HAVE ANY QUESTIONS DURING
THE PRESENTATION, PLEASE POST
THOSE IN THE Q&A WINDOW.
AND THEN WE WILL SAVE THOSE
UNTIL AFTER THE PRESENTATION AND
THEN WE'LL HOPEFULLY HAVE TIME
WHERE OUR PRESENTER CAN ANSWER
THE QUESTIONS.
SO, I'M DELIGHTED TO INTRODUCE
THE 2023 ADA LOVELACE LECTURER,
DR. LUCILA OHNO-MACHADO, DEPUTY
DEAN FOR BIOMEDICAL INFORMATICS
AND CHAIR AT DATA UNIVERSITY.
AS A DEPUTY DIRECTOR, I'M SORRY,
AS DEPUTY DEAN FOR BIOMARKER
INFORMATICS, DR. OHNO-MACHADO
OVERSEES RESEARCH ACROSS THE
YALE ACADEMIC HEALTH SYSTEM AND
HAS A LONGSTANDING CAREER
CONDUCTING RESEARCH IN
DEVELOPING PREDICTIVE MODELS AND
DATA SHARING AROUND CLINICAL
DATA.
PRIOR TO JOINING YALE SHE WAS AT
THE UNIVERSITY OF CALIFORNIA SAN
DIEGO AND WE WERE COLLEAGUES
THERE.
AND WHILE AT UCSD SHING
ORGANIZED AN INITIATIVE TO SHARE
DATA ACROSS THE FIVE UNIVERSITY
OF CALIFORNIA MEDICAL SYSTEMS
AND LATER EXTENDED THE APPROACH
TO VARIOUS INSTITUTIONS ACROSS
THE ENTIRE STATE OF CALIFORNIA
AND IN FACT AROUND THE COUNTRY.
PRIOR TO JOINING UCSD WAS A
CHAIR AT BRIGHAM AND WOMEN'S IN
BOSTON AND FACULTY OF HARVARD.
WELCOME AND THANK YOU FOR
JOINING US TODAY.
AND THE FLOOR IS YOURS.
>> THANK YOU SO MUCH, RICHARD,
FOR THIS OPPORTUNITY.
I WILL SHARE MY SCREEN AND I
WILL ASK YOU TO LET ME KNOW IF I
AM SHARING THE CORRECT MODE OR I
NEED TO DO SOME ADJUSTMENT IN
HERE.
LET ME JUST GO QUICKLY OVER THE
POWERPOINT PRESENTATION, JUST
ONE SECOND.
I'M JUST TRYING TO GET IT IN THE
PRESENTATION MODE HERE.
DOES THAT SOUND CORRECT?
>> YES.
>> THANK YOU SO MUCH.
WHAT I'M PLANNING TO DO IS
INTRODUCE MEDICAL A.I. WITHIN
THE FIELD OF BIOMEDICAL
INFORMATICS AND DATA SCIENCE AND
DESCRIBE SOME OF THE MODELS AND
HOW STATISTICALLY MACHINE
LEARNING CAN HELP PREDICT
CLINICAL OUTCOMES, BUT ALSO
INTRODUCE THE PROBLEM OF
EVALUATING PERSONALIZED
PREDICTION, SO A CRITICAL POINT
IN CLINICAL PRECISION MEDICINE
AND IDENTIFY TECHNICAL, SOCIAL,
ETHICAL CHALLENGES FOR HEALTH
EQUITY.
AND IN WHICH MEDICAL A.I. IS AN
IMPORTANT PORTION OF.
OBJECTIVES, I'LL GO THROUGH
DIFFERENT TYPES OF A.I.,
DEMYSTIFY A.I., WILL NOT FOCUS
ON GENERATIVE A.I. BUT TALK A
LITTLE BIT ABOUT IT BECAUSE
OTHERWISE I KNOW PEOPLE GET
DISAPPOINTED, NOT INTRODUCING A
LITTLE BIT ABOUT GENERATIVE A.I.
THEN USERS OF A.I. MODELS IN
HEALTH CARE AND ROLE OF
INFORMATICS IN DATA SCIENCE.
DIFFERENT TYPES ARE OUT THERE,
TERABYTES IN ELECTRONIC HEALTH
RECORDS, A WHOLE LOT OF OTHER
SOURCES.
AND WE FINALLY HAVE
COMPUTATIONAL POWER AND METHODS
TO DEVELOP MODELS THAT CAN BE OF
USE IN CLINICAL MEDICINE.
I SEE BIOMEDICAL INFORMATICS AND
DATA SCIENCE AS A SERIES OF
SUBSPECIALITIES, AND I LIST SOME
HERE.
AND I HIGHLIGHT EVALUATION
METHODS FOR DATA SHARING,
PRIVACY PRESERVING METHODS, THE
ONES I'M MORE CRITICALLY
INVOLVED WITH.
BUT IT TAKES, YOU KNOW, A
VILLAGE IN ORDER TO COME UP WITH
AN APPLICATION THAT CAN BE
DEPLOYED IN THE CLINIC.
SO, I SEE OURSELVES STRIVING TO
GET PRECISION HEALTH, PRECISION
MEDICINE, TO OUR PATIENTS, AND
THAT INVOLVES INTERSECTING
CIRCLES OF MEDICAL,
COMPUTATIONAL BIOLOGY AND PUBLIC
HEALTH COLLEAGUES WITH
ALGORITHMS, TOOLS, CONTROLLED
VOCABULARY, ONTOLOGY, DATA
MANAGEMENT, INFORMATION
RETRIEVAL TECHNIQUES.
IT IS NOT NEW.
I GO AFTER THE FIRST A.I.
SYSTEMS THAT WERE CALLED EXPERT
SYSTEMS IN THE '80s AND
'90s.
THEN NEURAL NETWORKS CAME UP IN
THE '90s AND SUCCESS AT THAT
DECADE BUT NOT IN THE FOLLOWING
ONE IN WHICH MOSTLY SUPPORT
VECTOR MACHINE APPLICATIONS WERE
OUT THERE, LIMITED APPLICATION.
THEN IN THE 2010s AND 20s WE
SAW RESURGENCE OF NEURAL
NETWORKS, ESPECIALLY DEEP
LEARNING.
AND NOW IN THE 20S AND 30s,
GENERATIVE A.I. IS WHAT'S
EXCITING MOST OF US, AND WE
DON'T KNOW WHAT'S COMING FOR THE
'30s AND '40s BUT I'M SURE
IT WILL BE EXCITING AS WELL.
THE NEW ENGLAND JOURNAL AND
OTHER CLINICAL SCIENTIFIC
PUBLICATIONS HAVE SPECIAL
ISSUES, EVEN SPINOFF JOURNALS
RELATED TO A.I.
JAMA AS WELL WITH A COLLECTION
OF ARTICLES FOCUSING ON LARGE
LANGUAGE MODELS, FOR EXAMPLE IN
OTHER A.I. TECHNOLOGIES.
IT'S OUT THERE.
IT'S POPULAR NOW, AS OPPOSED TO
THE TIMES WHERE WE STUDIED IT
AND IT WAS A VERY NICHE AREA.
I MUST REMIND YOU THAT ELISA,
THE FIRST CHATBOT, WAS 1966,
TERMINALS DIDN'T EVEN HAVE
MONITORS.
IT WAS ALL DONE ON PAPER AND
PRINTED HERE, LATER THEY GOT
THIS MONOCHROMATIC MONITOR.
TODAY IF YOU'RE INTERESTED IN
KNOWING HOW GOOD ELIZA WAS THERE
ARE EMULATORS, CAN YOU GO ONLINE
AND HAVE A CHAT.
IT'S ENTERTAINING.
EXPERT TEAMS CAME AFTER THAT,
DONE IN THIS MAINFRAME
COMPUTERS, MANY COMPUTERS AFTER
THAT.
THERE WAS CASSNET FOR GLAUCOMA,
MYCIN, AND INTERNIST, NOT
DIRECTLY DATA DRIVEN AS SYSTEMS
WE HAVE TODAY.
SO, DATA DRIVEN MODELS, HERE AN
EXAMPLE OF SUPERVISED LEARNING,
EXAMPLES WITH A GOLD STANDARD,
AND THEN YOU SELECT A MODEL IN
THIS CASE FOR EXAMPLE LINEAR
REGRESSION MODEL, AND YOU TUNE
ITS PARAMETERS UNTIL THE MODEL
FITS THE DATA.
THEN ONCE YOU HAVE A MODEL YOU
APPLY THIS MODEL TO PREVIOUSLY
UNSEEN CASES AND THEN YOU CAN
ESTIMATE THE OUTCOMES OF
INTEREST.
SO THIS IS, YOU KNOW, PRIOR TO
A.I.
THIS IS STATISTICS, IT'S BEEN
THERE MORE A WHILE.
MOST MODELS IN MEDICINE ARE
STILL BASED ON STATISTICAL
LEARNING OR REGRESSION MODELS OF
THIS TYPE.
A.I. FOR CLINICIANS AND
RESEARCHERS, THERE ARE THREE
COMPONENTS.
FIRST DATA.
DATA CAN BE FROM COHORT STUDIES,
ELECTRONIC HEALTH RECORDS, FROM
A LOT OF SOURCES.
THEN YOU HAVE MODELS IN WHICH
YOU CAN DO STATISTICAL MACHINE
LEARNING ON IT, WHILE PRESERVING
PRIVACY OF THE INDIVIDUALS.
AND THEN YOU HAVE
IMPLEMENTATION.
AND FOR IMPLEMENTATION YOU NEED
TO PACE SPECIAL ATTENTION FOR
PERFORMANCE MEASURES,
RESPONSIBLE USE OF A.I.
AND THE MODEL AS A MEDICAL
DEVICE, BECAUSE IT CAN CHANGE
CARE AND IT CAN CHANGE OUTCOMES.
I WILL NOT GO IN ORDER BUT I
WILL TOUCH ON THESE ITEMS
INDIVIDUALLY.
IN TERMS OF MODELS, THE
STATISTICAL AND MACHINE LEARNING
MODELS, MEDICAL A.I., THEY ARE
USED IN PRACTICE.
YOU HAVE, FOR EXAMPLE, THE MODEL
FOR END STAGE LIVER DISEASE
BEING USED AS CRITERION FOR
LIVER TRANSPLANTATION ORDER
PRIORITY.
YOU HAVE SEVERAL MODELS FOR
ACUTE KIDNEY INJURY, SEPSIS
PREDICTION, MANY ARE ALREADY
INTEGRATED INTO ELECTRONIC
HEALTH RECORD SYSTEMS.
AND THE LOCAL POPULATION, THEY
ARE NOT WELL CALIBRATED, I'LL
GIVE EXAMPLES SUBSEQUENTLY.
I SEE WE HAVE A HUGE OPPORTUNITY
HERE IN MEDICAL A.I.
I PUT THIS AS ONE IS THE
OPPORTUNITY TO DO NO HARM
BECAUSE UNFORTUNATELY IF MEDICAL
A.I. IS USED INAPPROPRIATELY IT
CAN CAUSE HARM.
TWO, TO PROMOTE HEALTH EQUITY.
AND WE'LL SEE WAYS IN WHICH WE
CAN DO THAT.
SO, FIRST I WANTED TO JUST GIVE
A VERY BRIEF OVERVIEW OF MEDICAL
A.I.
WHAT'S A.I. VERSUS REGRESSION
MODELS.
SO IMAGINE YOU HAVE LIKE HERE
JUST TO GIVE AN EXAMPLE, TWO
VARIABLES, AGE AND CERTAIN
LABORATORY VALUE.
AND YOU WANT TO SEPARATE THE
GREEN CASES, FOR EXAMPLE, THAT
DID NOT SURVIVE AND RED ONES
THAT DID SURVIVE.
CAN YOU USE REGRESSION MODEL,
HERE LOGISTIC MODEL, THE VALUES
OF INPUTS, COEFFICIENTS USED AND
THEN YOU RUN THROUGH A FUNCTION
AND IN THIS CASE LOGISTIC
FUNCTION, AND YOU GET A
PROBABILITY OF THE OUTCOME.
SO THE PROBABILITY COMING OUT OF
THAT.
SO THIS IS A TWO-DIMENSIONAL
EXAMPLE, AND YOU GET THIS
CONTINUOUS OUTPUT FOR AN OUTCOME
THAT IS EITHER GREEN OR RED.
SO BINARY OUTPUT.
BUT YOU GET A PROBABILITY OF,
FOR EXAMPLE, OF GREEN.
NOW, STATISTICAL LEARNING SUCH
AS LOGISTIC REGRESSION IS GOOD
FOR INTERPRETATION, WORKS WELL
FOR CERTAIN PROBLEMS, HAS VERY
FEW PARAMETERS, EASY TO COMPUTE,
AND IT'S EASY TO INTERPRET.
HERE AN EXAMPLE WITH THREE
VARIABLES AND SO ON.
DIFFERENT STATISTICAL SOFTWARE
MAY GIVE SLIGHTLY DIFFERENT
COEFFICIENTS BUT TYPICALLY IT'S
VERY SIMILAR.
ONCE THEY ARE TRAINED, THAT IS
ONCE THE MODEL IS FIT, THE
PARAMETERS DO NOT CHANGE, WILL
ALWAYS GENERATE THE SAME OUTPUT,
SO REPRODUCIBILITY IS PERFECT.
SOME MODELS ARE PROPRIETARY,
HOWEVER, BUT THEY ARE ALL
INTERPRETABLE WHICH FACTORS ARE
PROTECTIVE, MEANING WHICH ONES
DECREASE THE RISK OF DISEASE,
WHICH ONES INCREASE THE RISK OF
DISEASE, THIS IS VERY WELL
KNOWN.
THE MODELS ARE ONLY AS GOOD AS
DATA THEY ARE TRAINED ON,
HOWEVER, AND I'LL TALK A LITTLE
BIT MORE ABOUT THAT.
EXAMPLES HERE VERY POPULAR
FINDING CARDIOVASCULAR RISK
MODEL, AS I MENTIONED BEFORE.
SO WHY NEURAL NETWORKS CAME UP
IS BECAUSE THERE ARE CERTAIN
PROBLEMS BETTER SERVES, HAVING
MORE PARAMETERS, AND HAVING A
DIFFERENT TYPE OF MODELS THAT,
FOR EXAMPLE, CAN GIVE THE
SEPARATION THAT IS SHOWN IN THE
FIGURE HERE, FOR PROBLEMS IN
WHICH THERE IS NOT -- LINEAR
OPERATION IS NOT GOOD.
TO ILLUSTRATE AN ARTIFICIAL
NEURAL NETWORK OF THE TYPE OF
THE '90s, FOR EXAMPLE, WAS
MORE SHALLOW, HAD ONLY ONE
HIDDEN LAYER.
THE WAY IT WORKS IS HIM --
SIMILAR, IF YOU THINK OF LAYERS
OF LOGISTIC MODELS, PUT IT ALL
TOGETHER, A MORE COMPLEX, MORE
PARAMETERS, AND PERFORM HERE THE
VERY SAME FUNCTION WHICH IS TO
ESTIMATE BINARY OUTCOME BY
GIVING A CONTINUOUS OUTCOME
BETWEEN ZERO AND ONE, LIKE 60%
PROBABILITY OF DYING, FOR
EXAMPLE.
NEWLY NETWORKS WORK WELL, BETTER
FOR SOME TIMES OF PROBLEMS, HAVE
MORE PARAMETERS TO LEARN DATA,
REQUIRES MORE COMPUTATIONAL.
THEY ARE WAY MORE FLEXIBLE BUT
WAY COMPUTATIONALLY MORE
EXPENSIVE THAN REGRESSION
MODELS.
THEY ARE LESS INTERPRETABLE AS
WELL.
BUT THEY ARE REPRODUCIBLE WHICH
IS SOMETHING THAT SOMETIMES
PEOPLE DON'T UNDERSTAND.
IT'S WHEN YOU CALCULATE THOSE
WEIGHTS OR COEFFICIENTS, YOU CAN
FIX THEM, AND THEN YOU CAN
REPRODUCE YOUR RESULTS.
IT'S NOT THAT IT'S CONSTANTLY
BEING RETRAINED.
THAT'S UPON YOU TO CONTROL.
JUST WANTED TO DEMYSTIFY THAT.
AGAIN, THE DEEP LEARNING THAT
CAME BACK AT THE BEGINNING OF
THIS CENTURY IS NOTHING BUT MANY
MORE LAYERS OF NEURAL OR NODES,
ALSO MANY MORE NODES, SO YOU
HAVE THOUSANDS OF PARAMETERS IN
NEW MODELS OR LARGE LANGUAGE
MODELS MAY HAVE MILLIONS OF
PARAMETERS.
SO COMPUTATIONAL IT'S VERY HARD
TO DO.
IT TAKES A LONG TIME.
REQUIRES CERTAIN TYPES OF
HARDWARE, SOMETIMES, IN ORDER TO
GET THE NETWORK TO BE TRAINED.
THE WAY IT'S TRAINED IS THE SAME
AS PROPAGATION, THAT PROPAGATION
YOU START MANY TIMES RANDOM OR
START WITH A SET OF WEIGHTS,
TRANSFER LEARNING, ADJUST THOSE
UNTIL IT FITS THE DATA THAT YOU
HAVE AT HAND.
SO, AGAIN, THERE'S NO MAGIC IN
A.I. MODELS OF THIS KIND.
THEY ARE CLASSIFIERS.
THEY ARE JUST ADJUSTING THE
PARAMETERS UNTIL THEY FIT THE
DATA THEY ARE PRESENTED WITH.
NOW, I ALSO CAN SAY NEURAL
NETWORKS ARE NOT THE ONLY A.I.
TYPE OF METHODS.
ANOTHER ONE CALLED RANDOM FOREST
DERIVES ITSELF FROM
CLASSIFICATION TREES, IN
PICTURES THEY DO ORTHOGONAL
SEPARATIONS OF DATA AND KEEP
DOING IT, SO HERE FIRST YOU
DIVIDE INTO TWO, AND THEN AMONG
THOSE TWO YOU DIVIDE
ORTHOGONALLY, IN EACH QUADRANT
YOU KEEP DOING THE SAME UNTIL
YOU FIT THE DATA YOU HAVE.
SO THESE ARE CLASSIFICATION
TREES.
YOU START PARTITIONS, ACCORDING
TO ONE VARIABLE, THEN ACCORDING
TO THE SECOND VARIABLE.
IN A CLASSIFICATION TREES, THEY
ARE SIMPLE AND INTERPRETABLE.
HOWEVER RANDOM FOREST, IT'S
SEVERAL OF THOSE TREES,
CONSIDERED TOGETHER.
HIGHLY ADAPTABLE, WAY MORE
FLEXIBLE.
BUT NO LONGER INTERPRETABLE AS
THE CLASSIFICATION TREES WERE.
SO THOSE ARE A.I. MODELS, AGAIN
ALONG THE SAME LINES, MORE
FLEXIBILITY, LESS
INTERPRETABILITY.
SO, DATA-DRIVEN MODELS AND
MACHINE LEARNING IN STATISTICAL
MODELS, THERE ARE HUNDREDS OF
PREDICTIVE MODELS FOR DIAGNOSIS
AND PROGNOSIS OF DISEASE.
CLASSIFICATION CAN PERFORM
HUMANS, FOR EXAMPLE, BUT THE
WHOLE TRICK IS HOW YOU PRESENT
THIS DATA AND HOW YOU FORMULATE
SO THAT MODEL CAN FIT ITS
PARAMETERS TO YOUR DATA.
CLASSIFIERS SUCH AS THE ONES I
PRESENTED ARE THE MOST COMMON
TIME, DISCRIMINATIVE MODELS THAT
DISCRIMINATE FROM ONE CLASS TO
ANOTHER CLASS.
NOW, I COULD NOT GIVE THIS TALK
WITHOUT SAYING A LITTLE THING
ABOUT GENERATIVE MODELS.
GENERATIVE MODELS ARE DIFFERENT.
SUPPOSE YOU HAVE A SOURCE HERE
ON THE TOP, AND YOU WANT TO
GENERATE FAKE DATA, ARTIFICIAL
DATA, OUT OF THE SOURCE.
WHAT YOU DO IS THIS WAS DONE IN
PART IN RESPONSE TO SAYING,
WELL, NEURAL NETWORKS, THOSE
MODELS ARE NOT CREATIVE, THEY
CAN NOT GENERATE ANYTHING NEW.
WELL, GENERATIVE MODELS CAN.
THE WAY THEY DO IT, THEY HAVE
TWO COMPONENTS NOW.
ONE IS GENERATOR THAT PREDICTS
FAKE DATA.
THE OTHER ONE IS DISCRIMINATOR
NETWORK, EXACTLY THE CLASSIFIER
THAT I WAS TALKING TO YOU ABOUT
BEFORE.
SO, AGAIN, JUST QUICKLY,
TRAINING A GENERATIVE
ADVERSARIAL NETWORK AGAIN, WHICH
IS USED IN LARGE LANGUAGE MODELS
THAT CHAT GPT CONSISTS OF, REAL
DATA, YOU BUILD A GENERATOR THAT
CREATES ARTIFICIAL DATA, THEN
YOU THROW REAL DATA AND
ARTIFICIAL DATA INTO A
CLASSIFIER, IT CLASSIFIES INTO
REAL OR ARTIFICIAL.
SO THE GOAL HERE IS IF YOU CAN
CLEARLY CLASSIFY TO REAL AND
ARTIFICIAL YOU GIVE FEEDBACK TO
GENERATOR AND SAID, WELL, YOU'RE
NOT DOING REALISTIC ENOUGH DATA.
AND THEN THE GENERATOR ITERATES,
CREATES NEW TYPES OF ARTIFICIAL
DATA, YOU TRY THIS UNTIL
CLASSIFIER CAN NO LONGER TELL
THE DIFFERENCE, AND THAT'S WHEN
YOU TRAINED THIS GENERATOR IN A
WAY THAT PRODUCED FAKE DATA
THAT'S REAL ENOUGH.
SO, THIS IS ABOUT TRAINING
GENERATIVE ADVERSARIAL NETWORK
THAT, AGAIN, HAS MILLIONS OF
PARAMETERS AND COMPUTATIONAL
VERY EXPENSIVE.
HOWEVER, ONCE IT'S DONE, IT'S A
SET OF MULTIPLICATIONS YOU HAVE
TO DO.
SO USING IT AGAIN OR USING A
GENERATIVE ADVERSARIAL NETWORK
LOCALLY, FOR EXAMPLE,
CREATING -- USING A LARGE
LANGUAGE MODEL LOCALLY, ALL YOU
NEED IS A GENERATOR THAT
COMMENTS ALREADY PACKAGED, AND
THEN YOU USE YOUR DATA TO
GENERATE NEW DATA, IN THIS CASE
HERE IN CLINICAL MEDICINE IT
COULD BE BULLET POINTS ABOUT A
PATIENT, FOR EXAMPLE, OR
RESPONSE TO A PATIENT REQUEST.
AND THE GENERATOR CREATES TEXT
THAT ANSWERS THAT QUESTION, AND
THE ARTIFICIAL DATA IS CREATED.
THE PHYSICIAN VETS AND SAYS,
YES, IT'S CORRECT, OR, NO, IT'S
NOT CORRECT.
AND THEN IT RETURNS OR NOT TO
THE PATIENT.
NOW, PEOPLE HAVE BEEN SAYING,
WELL, BUT THEY HAVE --
GENERATORS HAVE HALLUCINATIONS
AND THEY COME WITH VERY
UNFEASIBLE DATA.
IT'S EXACTLY WHAT THEY WERE
CREATED FOR.
REMEMBER, THEY ARE -- THEY WERE
CREATED TO GENERATE NEW DATA, SO
I THINK IT'S SOMEWHAT UNFAIR TO
SAY IT'S HALLUCINATIONS, WHAT IT
REALLY IS THEY WERE NOT TRAINED
ENOUGH AND THEY WERE NOT TRAINED
IN YOUR TYPE OF DATA IN ORDER
NOT TO GENERATE SOMETHING THAT
WASN'T USEFUL.
SO, AGAIN, THIS FEEDBACK LOOP,
ONCE YOU STOP TRAINING, IT
DOESN'T EXIST.
SO THAT'S A PROBLEM BECAUSE IF
YOU WANT A LARGE LANGUAGE MODEL
TO BE TRAINING YOUR DATA, AGAIN,
THE COMPUTATIONAL EXPENSE WILL
BE HIGH, AND, YOU KNOW, IT'S NOT
SOMETHING EASY TO DO.
BUT IF YOU WANT IT TO BE TRAINED
TOGETHER WITH OTHER DATA, YOU
MIGHT NEED TO SUBMIT YOUR REAL
DATA ELSEWHERE WHICH ALSO MIGHT
CREATE A PRIVACY ISSUE.
IN TERMS OF DEMYSTIFYING MODELS
THERE ARE REASONS TO USE NEURAL
NETWORKS AND REGRESSION, AGAIN
LARGE LANGUAGE MODELS WITHOUT
LOCAL TRAINING ARE OF CONCERN.
ONCE IT'S CALCULATED, FIXED, IT
IS REPRODUCIBLE.
SO A.I. MODELS ARE REPRODUCIBLE.
LET ME TALK A LITTLE BIT ABOUT
PERFORMANCE MEASURES YOU SHOULD
BE AWARE FOR RESPONSIBLE USE OF
THOSE MODELS BECAUSE MODEL CAN
BE A MEDICAL DEVICE.
SO, WILL CLINICIANS USE IT, HOW
WILL THEY USE IT, IS THERE A
CONTROL GROUP BEFORE YOU FULLY
IMPLEMENT THAT IN CLINIC, WHEN
WILL THE MODEL BE EVALUATED OR
REEVALUATE AND HOW.
PERSONALIZED MEDICINE, A LOT OF
IT WILL DEPEND ON HOW GOOD THIS
PREDICTIVE MODELS ARE.
AND I GAVE THE EXAMPLE OF ACUTE
KIDNEY INJURY BEING A MAJOR
CAUSE OF MORBIDITY AND
MORTALITY.
THERE ARE HUNDREDS OF PREDICTION
MODELS FOR AKI OUT THERE, COMING
FROM ALL OVER THE PLACE.
MANY PREDICTIVE MODELS EXIST,
SOME ARE DEEP, SOME ARE NOT.
HOW THE MODEL IS EVALUATED.
SO THIS IS SOMETHING, A MODEL WE
PRODUCED USING UCSD DATA WHILE
AGO, WITH NEPHROLOGISTS.
IF YOU'RE FAMILIAR WITH
PERFORMANCE METRICS, AND AREA
UNDER THE CURVE, 81 IS
REASONABLE, WITHIN THE
CONFIDENCE INTERVALS.
PRESENTED HERE IS REASONABLE, SO
THIS MODEL COULD BE USED IN
PRACTICE.
HOWEVER, WHEN WE WERE VALIDATING
IT WITH A MAYO CLINIC COHORT, WE
NOTED A PARTICULAR PROBLEM.
IN HERE I'LL EXPLAIN WHY, BUT
YOU WANT THE ACTUAL PROBABILITY
FOR A GROUP OF PATIENTS TO BE
ALONG THE 45-DEGREE LINE THAT IS
ITS CLOSE TO ESTIMATED AVERAGE
PROBABILITY FOR THAT GROUP.
YOU SEE, FOR EXAMPLE, IF I GO
HERE TO THE ESTIMATED
PROBABILITY OF 0.4, 40% CHANCE
OF HAVING AKI FOR A PARTICULAR
GROUP, AT THE UCSD SAMPLE IT WAS
FINE, RELATIVELY CLOSE,
45-DEGREE LINE, WHEREAS AT THE
MAYO CLINIC THIS ACTUAL
PROBABILITY WAS ACTUALLY
DOUBLED, 0.8.
THEREFORE THE MODEL WAS NOT WELL
CALIBRATED, IN GENERAL, TO THE
MAYO COHORT, IF YOU SEE ALL THE
POINTS BEING ON THE UPPER SIDE
OF THE CURVE.
HOW DO YOU EVALUATE THESE
MODELS?
I'LL GO QUICKLY, JUST REMIND
YOU, YOU HAVE ON THE X-AXIS
PREDICTION ESTIMATES AND ON THE
Y-AXIS THE NUMBER OF PEOPLE.
LET'S SAY YOU HAVE A POPULATION
THAT IS NORMAL, AND A POPULATION
WITH A PARTICULAR DISEASE.
YOU HAVE ALMOST LIKE HAVING A
TEST, RIGHT?
HAVING THIS PREDICTIVE MODEL
SAYING IF IT'S ABOVE 50% THEN I
WILL DECLARE THE PERSON HAS
DISEASE, IF IT'S BELOW I WILL
DECLARE A PERSON DOES NOT HAVE
DISEASE.
AND WITH THAT, OF COURSE, YOU
WILL HAVE A LOT OF TRUE
NEGATIVES, TRUE POSITIVES, A FEW
FALSE NEGATIVES, A FEW FALSE
POSITIVES FOR THIS PARTICULAR
THRESHOLD OF 0.5.
NOW, EACH THRESHOLD WILL CREATE
A TWO BY TWO TABLE, YOU CAN
CALCULATE SPECIFICITY, POSITIVE
AND NEGATIVE PREDICTIVE VALUE,
OVERALL ACCURACY.
YOU JUST LOOK AT TWO BY TWO
TABLE, NOTHING LESS BUT THE REAL
GOALS ENTERED, NO DISEASE OR
DISEASE, AND WHAT THE ESTIMATE
IS FOR THAT PARTICULAR
THRESHOLD, NO DISEASE OR
DISEASE.
SO, KNOW THAT FOR A FACT, AND WE
CAN DO THE CALCULATION AND SOME
NUMBERS, SENSITIVITY OF 80%,
SPECIFICITY OF 90, AND SO ON,
THAT'S FOR THIS PARTICULAR
THRESHOLD.
NOW, YOU CAN DO ANOTHER
THRESHOLD IN WHICH YOU'RE
CONSERVATIVE AND WILL SAY PEOPLE
HAVE DISEASE WITH LOWER
THRESHOLD, AND YOU HAVE A
SENSITIVITY OF 100% AND
SPECIFICITY OF 80%.
OR YOU CAN GO IN THE MIDDLE LIKE
WE SAID OR YOU CAN GO MORE
LIBERAL AND YOU WILL SAY I WILL
ONLY DECLARE DISEASE IF I'M
REALLY SURE ABOUT IT, SO HIGHER
THRESHOLD FOR THIS ESTIMATE,
SPECIFICITY EQUALS 100%,
SENSITIVITY ONLY 60%.
SO I GAVE YOU THREE THRESHOLDS
AND THREE TWO BY TWO TABLES,
RIGHT?
SO IF YOU PUT THEM ALL ON A PLOT
OF SENSITIVITY OVER ONE MINUS
SPECIFICITY YOU HAVE AN ROC
CURVE, NOTHING BUT THE PLOT OF
THE TWO VALUES.
AND ROC CURVES YOU'RE MORE USED
TO THEY ARE MORE CONTINUOUS LIKE
THIS.
NOW, YOU CAN TELL FROM THIS THAT
IF YOU HAVE 45-DEGREE LINE THAT
MEANS NO DISCRIMINATION, NO
VALUE IN THIS CLASSIFIER, AND IF
YOU HAVE -- THAT COMES WITH AN
AREA UNDER THE ROC CURVE OF 0.5.
NOW, YOU TYPICALLY HAVE AN AREA
THAT IS BETWEEN 0.5 AND 1,
OTHERWISE YOUR MODEL DOESN'T DO
ANYTHING.
AND YOU YOU CALCULATE THINGS
THIS WAY.
THERE ARE MANY PAPERS THAT COME
ONLY WITH PERFORMANCE METRIC OF
THE AREA UNDER THE ROC CURVE
WHICH IS A DISCRIMINATIVE
PERFORMANCE.
BASED ON SENSITIVITY AND
SPECIFICITY ALONE.
BUT THEY DON'T TELL ANYTHING
ABOUT THE QUALITY OF THOSE
ESTIMATES.
THEY TALK ABOUT THE ORDER BUT
NOT THE QUALITY.
LET ME GIVE AN EXAMPLE OF HOW
THIS IS.
LET'S SAY OF THE ORIGINAL
ESTIMATES, LET'S SAY WE ONLY
HAVE 20 PATIENTS AND WE
ESTIMATED A VALUE FOR THOSE WHO
WERE ALIVE AND DECEASED, THESE
ARE THE NUMBERS.
I ORDERED THEM HERE AND PUT IN
THE TWO COLUMNS.
AN INTERPRETATION OF THE AREA
THAT UNDER ROC CURVE IS THE
CALCULATION OF HOW MANY TIMES IF
I DO PAIRS OF ALIVE AN DECEASED
PATIENTS, HOW MANY TIMES I'M
RIGHT, MEANING THIS VALUE FOR
ALIVE HAS TO BE SMALLER THAN A
VALUE FOR THE DECEASED.
IF EVERYTHING WERE PERFECT, THEN
YOU WOULD HAVE A DIVIDING LINE,
LET'S SAY AT 0.3, AND AFTER THAT
EVERYONE WOULD HAVE DISEASE OR
NOT.
BUT AS YOU CAN SEE, THAT'S NOT
THE CASE.
SO, ONE INTERPRETATION IS TO
CALCULATE THE AREA UNDER THE ROC
CURVE, AS THE NUMBER OF
CONCORDANT PAIRS PLUS BY 100
PAIRS, TEN ON THIS SIDE, TEN ON
THIS OTHER SIDE.
IF YOU DO THAT, YOU WILL SEE
THAT YOU WILL CALCULATE AN AREA
UNDER THE ROC CURVE, IN THIS
CASE 0.785.
AND YOU SAY, WELL, THAT'S A GOOD
MODEL.
ACCEPTABLE MODEL.
HERE ARE THE ESTIMATES.
HOW DO I KNOW THESE ESTIMATES
ARE CORRECT?
AND THE ANSWER IS I DON'T,
BECAUSE ROC CURVES DON'T DO
THAT.
IN FACT, IF YOU DIVIDE ALL
ESTIMATES BY 10, AS I'M SAYING
HERE, THE ORDER REMAINS THE
SAME, THE NUMBER OF CONCORDANT
AND DISCORDANT PAIRS REMAINS THE
SAME.
YET IT'S MUCH BETTER, RIGHT?
RISK FOR DISEASE, I WOULD MUCH
RATHER HAVE A RISK OF 2.7% THAN
27% RISK.
SO, THIS IS CALLED, YOU KNOW,
DIFFERENCES IN CALIBRATION OF
THE MODELS, THERE ARE METHODS TO
RECALIBRATE WHICH WE'VE BEEN
APPLYING, AND IN THIS CASE YOU
SEE THE BEST RECALIBRATION WOULD
BRING BAD PARTICULAR ESTIMATE,
ALL THE WAY FROM 27 TO 78%,
WHICH IS A HUGE DIFFERENCE.
SO, THE WAY YOU APPLY THE MODELS
IN CLINICAL MEDICINE, IT'S
EXTREMELY IMPORTANT THAT THE
MODELS BE CALCULATED.
AND THIS CAN BE SEEN HERE THAT
ALTHOUGH THE AREA UNDER THE ROC
CURVE IS EXACTLY THE SAME, THERE
ARE SIGNIFICANT DIFFERENCES IN
TERMS OF ABSOLUTE ERROR OF THE
ESTIMATES, AND SO ON.
THIS IS A WHOLE NEW AREA THAT
UNFORTUNATELY JOURNAL EDITORS
HAVE NOT ALWAYS EMPHASIZED, BUT
IT'S CRITICALLY IMPORTANT FOR
CLINICAL MEDICINE.
THERE ARE CALIBRATION INDICES IN
THE, AGAIN, ESTIMATING THE
PROBABILITY IS REASONABLY
CORRECT.
AND ONE WAY TO THINK ABOUT THIS
IS HOW WELL DO PREDICTED
PROBABILITIES MATCH THE ACTUAL
PROBABILITIES, FOR EXAMPLE,
PROGNOSIS OF CANCER AND SO ON.
ACTUAL PROBABILITY OF METASTASIS
IS 10% FOR A PATIENT, IF 10 OUT
OF 100 PATIENTS JUST LIKE THIS
DEVELOP CANCER, THE WHOLE
PROBLEM IS TO DECIDE WHO IS JUST
LIKE THAT PATIENT, HOW YOU
CALCULATE THAT NEIGHBOR PATIENTS
FOR THAT PARTICULAR CASE.
AND THE WAY IT IS DONE, IT IS
IMPERFECT BUT IT HAPPENS TODAY,
IS THAT YOU SORT ALL THE SYSTEM
ESTIMATES, AVERAGE WITHIN THAT
GROUP, YOU COMPARE TO THE
PROPORTION OF EVENTS THAT
ACTUALLY HAPPENED IN THAT GROUP.
SO ON THE RIGHT-HAND SIDE IS THE
REAL OUTCOMES, GOLD STANDARD, ON
THE LEFT-HAND SIDE IS THE SYSTEM
ESTIMATES.
THIS IS ANY A.I. SYSTEM.
ANY STATISTICAL LEARNING SYSTEM.
AGAIN, YOU CAN CALCULATE THIS
WAY, AND THEN PLOT THAT
CALIBRATION CURVE THAT I
MENTIONED TO YOU ABOUT, AND THAT
WAS SO DIFFERENT BETWEEN THE
EVALUATION DONE AT UCSD VERSUS
THE EVALUATION DONE AT MAYO
CLINIC.
SO, AGAIN, METHODS EXIST, NOT
ALWAYS USED, AND IT'S CRITICALLY
IMPORTANT TO VALIDATE THE MODEL
IN THE POPULATION THAT IT'S
GOING TO BE USED, BUT THAT'S NOT
ALWAYS DONE.
AGAIN, HERE JUST SHOWING HOW
UNDERESTIMATION WOULD HAPPEN IF
WE HAD APPLIED THE UCSD MODEL TO
THE MAYO COHORT WITHOUT
ADJUSTMENTS.
NOW, THERE WAS A REVIEW OF
SYSTEMATIC REVIEWS FOR MODELS OF
ACUTE KIDNEY INJURY, AGAIN
THERE'S SO MANY OF THEM, WHAT
AUTHORS FOUND IS THAT IN THIS --
EACH ONE OF THESE REVIEWS OF
THIS, THIS IS SYSTEMATIC, SO
EACH OF THE SYSTEMATIC REVIEWS,
THE MODELS PRESENTED HAVE VERY,
VERY FEW EXTERNAL VALIDATIONS SO
YOU TAKE THE FIRST ROW,
CORONARY, ANGIO PLASTIC PATIENTS
ONLY, THERE WERE 70 PREDICTION
MODELS WITH INTERNAL VALIDATION,
MEAN THEIR OWN SITE THEY
PERFORMED WELL, BUT THERE WERE
ONLY 9 WITH EXTERNAL VALIDATION.
SO, WHAT DOES THIS MEAN?
IT MEANS YOU CANNOT JUST CREATE
A MODEL IN YOUR PLACE AND NOT
VALIDATE ELSEWHERE.
IN FACT, THE MORE YOU CAN SHARE
HOW YOU DID YOUR MODEL AND
PERHAPS AGGREGATE DATA FROM
OTHER SITES, THE BETTER IT IS IN
EXTERNAL VALIDATION.
AGAIN, ALWAYS NECESSARY.
SO, NOW WE GET INTO POLYGENIC
RISK SCORES FOR PRECISION
MEDICINE.
WE'LL GET TO THAT POINT.
AND THEY ARE GOING TO ASSESS
RISK FOR MANY DISEASES.
CAN WE IMPROVE THOSE METHODS?
CAN THOSE METHODS BE
CONTINUOUSLY UPDATED?
CAN WE DO THIS WITHOUT
INTRODUCING MORE BIAS THAT LEADS
TO MORE DISCRIMINATION OF
PEOPLE, CAN WE PROTECT PRIVACY
OF THE INDIVIDUALS?
WHAT TO DO WITH INDIVIDUALS FROM
MIXED ANCESTRY?
SO A LOT OF QUESTIONS NOW, AND
WE KNOW FROM A.I. THAT WE NEED A
LOT OF DATA.
HOW CAN WE DO THIS?
WELL, WE NEED TO BUILD ACCESS TO
LARGE REPOSITORIES TO IMPROVE
RESEARCH.
WE NEED TO DO THIS IN A WAY THAT
PRESERVES PRIVACY AND AGGREGATE
DATA FROM DIFFERENT COUNTRIES
AND USE FOR NEW ANALYSIS.
THAT GOES A LITTLE BIT BACK
TOWARDS THE HOW TO PROTECT
PRIVACY WHILE BUILDING
EVALUATION MODELS, WHICH WE'VE
BEEN VERY INTERESTED IN, BECAUSE
WE USE ELECTRONIC HEALTH RECORDS
A LOT, AND WE THINK IS IT RIGHT
TO SHARE BECAUSE PEOPLE HAVE NOT
ALWAYS BEEN ASKED THAT THEIR
DATA BE USED FOR RESEARCH.
IS IT RIGHT NOT TO SHARE?
BECAUSE NEW DISCOVERIES DEPEND
ON SHARING, BUILDING GOOD MODELS
DEPENDS ON HAVING A LOT OF DATA.
CAN WE SHARE WITHOUT MOVING DATA
AROUND, ON FEDERATED ANALYSIS,
IS THIS PRACTICAL, IS IT
ACCURATE?
SO ITEMS THAT WE HAVE LOOKED FOR
CERTAIN TIME, AND JUST HERE TO
GIVE THE MOTIVATION,
COMBINATIONS OF VALUES CAN
IDENTIFY PEOPLE SO EVEN THOUGH
YOU MIGHT NOT ENTER BIOMETRIC
DATA YOU HAVE UNIQUE
COMBINATIONS, YOU CAN -- YOU
KNOW SOMETHING ABOUT ELIZA, YOU
KNOW A AND B, YOU GO TO THE
TABLE OF THE STUDY AND YOU MATCH
THAT.
IN FACT, YOU DON'T EVEN NEED
UNIQUENESS.
IF ALL THE ENTRIES YOU FIND, A
EQUALS 10, B EQUALS 20, HAVE THE
SAME DIAGNOSIS OR IF 99% IT, SO
ON.
THERE ARE A LOT OF ISSUES IN
PRIVACY THAT EVEN SO CALLED
DE-IDENTIFIED DATA NEEDS TO BE
EXAMINED.
GENOME ARE THE BEST BIOMETRICS,
SHOULD THEY BE REGULATED THE
SAME AS OTHER IDENTIFIERS IN
HIPAA?
THAT'S SOMETHING WE SHOULD THINK
ABOUT.
THEY SHOULD BE TREATED AS
IDENTIFIERS BECAUSE NOT ONLY
THEY ARE GOOD IDENTIFIERS,
BETTER THAN FINGERPRINTS AND
OTHER THINGS BECAUSE THEY CAN
BRIDGE THE PRIVACY OF RELATIVES,
AS YOU KNOW, FROM THE PRESS.
SO, WE HAVE DONE A LOT OF WORK
IN INSTITUTIONAL PEOPLE CENTRIC
STUDIES OF CONSENT, CONSENT
MANAGEMENT, TIERED CONSENT AND
SO ON BUT ALSO PRIVACY
TECHNOLOGY PRACTICES THAT HELP
WITH PROTECTING THE PRIVACY, ONE
OF THEM IS FEDERATED
COMPUTATION, AGAIN BY ITSELF IT
DOESN'T PROTECT PRIVACY BUT DOES
GIVE SOME LEVEL OF COMFORT THAT
THE DATA ARE NOT GOING OUT THERE
AND BEING UNCONTROLLED.
SO, COMPUTING WITH DISTRIBUTED
DATA, THERE ARE SEVERAL
ALGORITHMS TO DO THAT.
SO THAT EACH INSTITUTION KEEPS
THEIR OWN DATA BUT WE CAN
DEVELOP AN A.I. MODEL OUT OF
THAT.
THERE ARE TWO TYPES OF
PARTITIONS DIDACTIC, WE COMBINE
THEM IN THE ANALYSIS OR VERTICAL
IN WHICH ONE INSTITUTION HAS
SOME PART OF DATA, THE OTHER
INSTITUTIONS HAVE THE OTHER
PART, AND WE NEED TO COMPUTE
WITH THEM WITHOUT PUTTING THIS
DATA TOGETHER, WITHOUT
CENTRALIZING THE DATA.
SO WE HAVE DONE THAT, AS WAS
MENTIONED BEFORE, WITH CLINICAL
DATA RESEARCH NETWORKS.
AGAIN, NESTED HUBS, WE CAN
CALCULATE WITH, FOR EXAMPLE,
ELECTRONIC HEALTH RECORDS FROM
THE V.A. IF WE ARRANGE THE
NETWORK IN CERTAIN WAYS SO THAT,
AGAIN, WE DON'T NEED TO MOVE ANY
DATA AROUND.
DURING COVID, WE DID THAT FOR
DISCOVERY FROM CLINICAL RECORDS
IN WHICH THE CLINICIANS DIDN'T
KNOW THE SAFETY OF CERTAIN
MEDICATIONS, FOR PATIENTS WITH
COVID-19, AND WE WERE ABLE TO
CONSULT THE LARGE NETWORK AND
DETERMINE IF THERE WERE ANY
SIGNALS THAT SOME MEDICATIONS
WOULD BE UNSAFE.
SO WE DID THAT WITH A WHOLE V.A.
NETWORK, WITH INSTITUTIONS SHOWN
HERE IN RED, AS WELL AS ONE
INSTITUTION IN GERMANY THAT,
AGAIN, BECAUSE WE COULD DO THIS
ALL IN A FEDERATED MANNER WAS
ABLE TO PARTICIPATE.
IT WAS IN THE BEGINNING OF THE
PANDEMIC, AND WE COULD CONSULT
NOT ONLY WITH PATIENTS WITH
COVID BUT ALSO CONTROLS.
WE DID FEDERATED ANALYSIS, IN
THIS CASE SIMPLE LOGISTIC
REGRESSION TO EVALUATE AND USING
DATA FROM SEVERAL INSTITUTIONS,
WE DID NOT HAVE DIFFERENCES IN
MORTALITY RELATED TO RACE AND
ETHNICITY, AND COMFORTING FOR
ALL THE INSTITUTIONS INVOLVED,
BUT ALSO MEANING THAT WE
PROBABLY JUST GET THE PATIENTS
HOSPITALIZED AND WE'RE MISSING A
LOT OF OTHER -- THOSE WHO DID
NOT GET INTO THE HEALTHCARE
SYSTEM.
WE ALSO HAVE WORKED WITH TRIBAL
NATIONS, IN WHICH THE STRONG
HEART STUDY COLLABORATORS WORKED
WITH THEIR GROUPS IN ARIZONA,
OKLAHOMA, NORTH AND SOUTH
DAKOTA, AND THE INTERESTING
METHODOLOGIC PROBLEM HERE IS
THAT THE PHENOTYPES ARE
CENTRALIZED IN THE OKLAHOMA
COORDINATING CENTER, BUT THE
GENOTYPES ARE CENTRALIZED IN
TEXAS.
SO, THERE ARE TWO INSTITUTIONS,
AGAIN, THAT HAVE DIFFERENT
PORTIONS OF THE PARTICIPANTS'
RECORDS, WE SHOWED THROUGH
VERTICAL PARTITIONS ANALYSIS
FEDERATED ANALYSIS THIS WAY, WE
COULD COMPUTE WITH THIS DATA
TOGETHER WITHOUT MOVING THE DATA
AROUND SO THERE'S AN ALGORITHM
THAT'S BEEN PUBLISHED,
SUBSEQUENT ONES TOO IN WHICH WE
USED SPECIFIC TECHNIQUES TO
LEARN GLOBAL REGRESSION
PARAMETERS, DO THIS IN AN
ITERATIVE PROCESS WITHOUT,
AGAIN, MOVING DATA AROUND.
WE ARE ALSO CURRENTLY WORKING ON
HORIZONTAL PARTITIONS, AND
WORKING WITH THE "ALL OF US"
PROGRAM AND MILLION VETERAN
PROGRAM TO DO PHENOTYPE,
GENOTYPE, TESTS ON HORIZONTALLY
PARTITIONS, WORKING ON LOCAL
ANCESTRY FOR GENOTYPES AND
PARTICULARLY WE HAVE A CENTER OF
EXCELLENCE IN GENOME SCIENCE
FOCUSED ON THE STUDY OF ADMIXED
INDIVIDUALS OFTEN LEFT OUT
BECAUSE IT'S HARDER TO COMPUTE.
ON THE CONTRARY WE BELIEVE
INCLUSION OF ADMIXED INDIVIDUALS
IS CRITICAL FOR ANALYSIS.
WE'RE METHODOLOGISTS BUT AT THE
CORE IS THE DATA.
NO MATTER HOW A.I. FAIRNESS WE
STUDY AND TRY TO FIX PROBLEMS
WITH REPRESENTATION OF CERTAIN
GROUPS OF INDIVIDUALS, THEY ARE
NO SUBSTITUTE TO FIXING THE DATA
THEMSELVES, MEANING COLLECTING
THE DATA FROM MORE DIVERSE
POPULATIONS.
SO WE'VE BEEN DOING THAT THROUGH
CLINICAL DATA NETWORKS, ALSO NOW
AS PART OF THE "ALL OF US"
PROGRAM.
SO PRECISION X, X COULD BE
MEDICINE, NURSING, PUBLIC
HEALTH, ANY OTHER HEALTH-RELATED
DISCIPLINE, IT REQUIRES
DIVERSITY.
THE GENOMIC, FOR EXAMPLE,
VARIANTS OF SIGNIFICANCE HAVE
BEEN SHOWNING TO SIGNIFICANT IN
CERTAIN POPULATIONS,
PARTICULARLY EUROPEANS, SOME
ASIAN POPULATIONS WELL
REPRESENTED BUT NOT ALL.
THE PHENOTYPIC DATA AS WELL, WE
OPERATE ON ELECTRONIC HEALTH
RECORDS, BUT WHAT ABOUT DATA
FROM THOSE WHO DON'T HAVE AN
ELECTRONIC HEALTH RECORD?
ALSO PERSONAL MONITORING,
BEHAVIOR DATA, ET CETERA.
ENVIRONMENTAL DATA, IS SOMETHING
WE NEED TO WORK A LOT ON BECAUSE
EVERYONE IS DIFFERENT.
AND CONCEPTS OF LABELING SOMEONE
FROM A CERTAIN DEMOGRAPHIC AS A
PROXY FOR ENVIRONMENTAL
EXPOSURES OR DIET, FOR EXAMPLE,
ARE NOT CORRECT.
SO, "ALL OF US" RESEARCH PROGRAM
THAT'S WHY WE AS INFORMATICS
PEOPLE GOT EXCITED ABOUT IT
BECAUSE THIS IS OUR CHANCE TO
HELP COLLECT DATA AT SCALE FROM
DIVERSE INDIVIDUALS WHO FORMERLY
WERE UNDERREPRESENTED IN
BIOMEDICAL RESEARCH.
CURRENT PROTOCOL, YOU CAN FIND
AN EXCITING COLLECTION OF ITEMS,
CONSENT IS VERY DETAILED, VERY
STUDIED, SO VERY COMFORTABLE
ASSISTING WITH THAT INITIATIVE.
THE CALIFORNIA CONSORTIUM THAT
WE LED HAS MORE THAN 50,000
PARTICIPANTS SO FAR, EXTENDING
TO THE EAST COAST AND PUERTO
RICO, AS WELL.
WE HOPE TO RECRUIT 10% OF THE
PARTICIPANTS FOR THE "ALL OF US"
CONSORTIUM AS WELL AS
PARTICIPATE IN VARIOUS
INITIATIVES OF ETHICS AND
SCIENCE COMMITTEE AND A WHOLE
LOT OF OTHER EXCITING THINGS
THAT ARE HAPPENING IN THERE.
SO, FOR US, INFORMATICS, MEDICAL
A.I. PEOPLE, DOCUMENTING LACK OF
DIVERSITY IS NOT ENOUGH.
WE NEED TO HELP REALIZE THIS
DREAM OF PERSONALIZED HEALTH
CARE, BY HELPING WITH ETHICAL
COLLECTION OF DATA FROM DIVERSE
POPULATIONS, ON NEW METHODS TO
ENGAGE AND RETAIN DIVERSE
POPULATIONS, NEW ALGORITHMS AND
TOOLS TO ANALYZE THIS DATA, AND
HELP DISSEMINATE FINDINGS TO
VARIOUS CONSTITUENCIES, THEY
CANNOT BE LIMITED TO THE
SCIENTIFIC COMMUNITY ONLY.
SO OUR VISION IS THAT NO ONE
WILL BE LEFT OUT, THEY WILL
REPLACE DEMOGRAPHICALLY WITH
OBJECTIVE VARIABLES IN THESE
MODELS AND WILL DEVELOP NEW
METHODS AND TOOLS THAT ALLOW FOR
GENETIC FINDINGS, ANALYSIS OF
CLINICALLY RELEVANT DATA,
EVENTUALLY PRECISION X TO BE
APPLICABLE TO ALL.
WITH THAT I WILL LEAVE SOME TIME
FOR QUESTIONS AND I WANT TO
ACKNOWLEDGE A LOT OF PROJECTS
THAT HELPED US GET TO WHERE WE
ARE, AND A LONG LIST OF
COLLABORATORS AND IN EACH ONE
OF THESE PROJECTS.
AGAIN, THANK YOU ALL FOR
ATTENDING.
>> THANKS, LUCILA.
YOU COVERED A LOT OF TERRITORY.
INTERESTING.
I LEARNED A LOT.
WE HAVE A NUMBER OF QUESTIONS.
WHY DON'T WE DIVE IN.
FIRST ONE FROM ALI, WHAT UNMET
HEALTH CHALLENGES DO YOU FORESEE
A.I. SOLVING WITHIN THE NEXT
DECADE?
>> I THINK A NEED TO ASSIST AND
MAKE HEALTHCARE PROVIDERS MORE
FOCUSED ON WHAT THEY ARE GOOD
AT, LESS FOCUSED ON THE
PAPERWORK AND OTHER THINGS.
I THINK THAT WILL HELP A LOT.
PATTERN RECOGNITION OF THINGS
THAT ESCAPE THE EYE OF HUMANS
WILL BE DEFINITELY OUT THERE.
AND ANY, AGAIN, ASSISTANCE WITH
CHAT I THINK CHATBOTS WILL
IMPROVE OVER TIME AND MIGHT BE
ABLE TO SOLVE SIMPLE THINGS.
THEN RESERVE THE SPECIALIZED
TIME FOR MORE COMPLEX
SITUATIONS.
>> YOU MADE THE INTERESTING
COMMENT ABOUT THESE GENERATIVED
ADVERSARIAL WHERE YOU TRAIN THEM
NOT TO DISTINGUISH BETWEEN TRUE
AND ARTIFICIAL BUT WE REALLY
WANT TO GENERATE TRUE
INFORMATION FROM THE GENERATIVE
MODELS.
I WONDER IF THERE'S A WAY OF
TURNING IT AROUND TO AVOID THE
ARTIFICIAL.
>> OH, THAT'S ONLY BECAUSE IF
YOU TRAIN THEM WITH YOUR DATA,
AND WITH YOUR SCRUTINY OF THE
CLASSIFIERS, YES, IT'S CORRECT.
I WILL GO BACK TO IMPROPER USE
OF SOME METHODS AND MODELS THAT
WERE CREATED FOR ONE PURPOSE AND
NOW WE'RE TRYING TO ADAPT TO
SOME OTHER PURPOSE WITHOUT
THINKING ABOUT THE TRAINING
SETS, HOW THE CORRECTIONS ARE
MADE.
>> YEAH, AND I THINK ONE OF THE
THINGS THAT CONCERNS ME IS
PEOPLE WHO ARE USING THESE A.I.
APPROACHES WITHOUT REALLY
UNDERSTANDING THE DETAILS ABOUT
WHAT THEY ARE DOING.
AND I THINK THAT'S VERY
DANGEROUS.
THE NEXT QUESTION IS HOW DO YOU
HANDLE MEDICAL IMAGE DATA LIKE
MRIs, CTs, ET CETERA?
>> YEAH, AGAIN, I THINK THERE
ARE -- THAT'S IMAGING IS ONE
AREA THAT A.I., YOU KNOW, HAS
PROVEN TO BE SO HELPFUL AND WILL
CONTINUE TO DO SO.
IMAGES ARE ALSO VERY HARD TO
DE-IDENTIFY, RIGHT?
SO THAT MAKES IT ALSO HARD TO
SHIP OUT ALL THE IMAGES TO SOME
VERY INTENSE COMPUTE CLUSTER
SOMEWHERE, THAT WE CANNOT
NECESSARILY TRUST.
BUT I THINK THE TRUST ISSUE IS
RESOLVED WITH AGREEMENTS, AND
WITH LEGAL SUPPORT, SO I DON'T
SEE THAT AS UNSURMOUNTABLE.
I JUST THINK WE NEED TO, YOU
KNOW, MAKE SURE WE PROPERLY
HANDLE THE PRIVACY OF THE
PATIENTS AND PARTICIPANTS.
>> , YOU KNOW, EXTENDING THAT,
DO YOU THINK IF YOU CONVERT
IMAGES INTO FEATURES THAT THEN
IT'S MORE -- YOU KNOW, IT'S
HARDER TO REIDENTIFY THE
INDIVIDUAL?
>> YEAH, THAT COULD BE SO, BUT
EVEN THAT CONVERSION, RIGHT, IF
YOU USE AUTOENCODER OR SOME
OTHER THING, THAT REQUIRES
COMPUTATIONAL POWER THAT IN
TIMES IS NOT EASY TO OBTAIN.
>> YEAH, YEAH.
NEXT QUESTION, DO YOU THINK THE
USE OF SYNTHETIC DATA IS A
SOLUTION FOR DATA PRIVACY.
>> I DON'T.
I THINK SYNTHETIC DATA MAY BE
USEFUL, LET'S PUT IT, FOR
EXAMPLE, ELECTRONIC HEALTH
RECORD DATA, SYNTHETIC, IT MAY
BE USEFUL TO TRAIN SOMEBODY TO
OPERATE AN ELECTRONIC HEALTH
RECORD SYSTEM WITH REALISTIC
DATA, FOR EXAMPLE.
NOW, TO CREATE A MEDICAL A.I.
SYSTEM THAT LEARNS FROM THAT
DATA I WOULD SAY I DON'T THINK
IT'S USEFUL BECAUSE SYNTHETIC
DATA, IF IT IS OF THE SOURCE
THAT SOME INSTITUTIONS OR
COMPANIES PORTRAY AS RETAINING
EVERY SINGLE ASSOCIATION THAT
WAS AVAILABLE IN THE ORIGINAL
DATA, IF YOU HAVE THAT THEN YOU
ACTUALLY DON'T NEED SYNTHETIC
DATA BECAUSE YOU ALREADY HAVE,
YOU KNOW, THE MODEL THAT
GENERATES ALL OF THAT SO YOU
HAVE THE KNOWLEDGE THAT YOU WERE
LOOKING FOR.
SO I WOULD SAY I DON'T BELIEVE
IN SYNTHETIC DATA.
MY COLLEAGUES ALSO SAY, WELL,
MEDICAL DATA IS SO NOISY
ALREADY, WHY ARE YOU TRYING TO
PUT IN EVEN MORE NOISE INTO DATA
AND SO ON.
I DO BELIEVE IN AGREEMENTS.
I BELIEVE IN TRANSPARENCY OF
USER DATA.
SYNTHETIC DATA ARE DERIVED FROM
REAL DATA, RIGHT?
TO THE EXTENT THEY ARE STILL
SOME PRIVACY RISK THERE, THAT
HAS TO BE, BECAUSE IF YOU
ONLY -- THE ONLY WAY TO PRESERVE
PRIVACY IS REMOVE ANY SIGNAL
FROM THE DATA AND MAKING IT
USELESS.
>> NEXT QUESTION, COULD YOU
PLEASE COMMENT MORE ON THE BEST
PRACTICE FOR DE-IDENTIFYING
DATA, BUT PRESERVING ITS
CAPABILITY OF LINKING TO OTHER
DATA SOURCES WHEN NEEDED, ANY
CONCERNS REGARDING PRIVACY AND
ETHICS?
>> I HAVE A LOT OF CONCERNS WITH
LINKAGE OF DATA THAT IS NOT
CONSENTED SIMPLY BECAUSE THE
LONGER -- THINK OF THE RECORD AS
HAVING A CERTAIN SIZE, AND
REMEMBER YOU CAN MATCH
INFORMATION YOU KNOW AND THEN
IDENTIFY AN INDIVIDUAL, THE
TARGET INDIVIDUAL IN THAT
DATASET.
THE LONGER YOU MAKE THE RECORD
THE MORE CHANCES TO IDENTIFY THE
INDIVIDUAL, RIGHT?
THE LINKAGE ITSELF INCREASES THE
RISK OF PRIVACY BREACH BY A LOT,
WHERE THE PEOPLE WHO ARE
CONSENTING FOR LINKAGE
UNDERSTAND THAT IS UNCLEAR, AND
WHETHER PEOPLE ARE EVEN
CONSENTING FOR THAT, IT'S ALSO
UNCLEAR.
SO I THINK THERE ARE TREMENDOUS
ETHICAL ISSUES TO BE RESOLVED,
BUT OF COURSE KNOWING THAT
LINKAGE HELPS RESEARCH BECAUSE
IT MAKES THE REFERENCE MORE
COMPLETE, LESS -- DEDUPLICATE
RECORDS.
WITH EVERYTHING, IT'S A BALANCE,
I BELIEVE.
>> UH-HUH, YEAH.
TWO MORE QUESTIONS.
IS THE LEGAL SYSTEM READY TO
HANDLE MEDICAL MISTAKES MADE BY
A.I.?
IN OTHER WORDS, WHO IS TO BE
SUED IF A CERTIFIED A.I.-BASED
SYSTEM CAUSES HARM?
>> THAT'S A GOOD QUESTION.
I DON'T THINK THE STRUCTURE IS
THERE.
I CAN SAY THAT THERE HAS BEEN A
LOT OF DISCUSSION AND CURRENT
PANEL HAVE BEEN IN WHICH WILL
ISSUE A PAPER VERY SOON ON
RACIAL AND ETHNIC DISCRIMINATION
IN A.I. MODELS, FOR EXAMPLE,
WHICH IS, YOU KNOW, BEYOND
MISTAKES, RIGHT?
BECAUSE DEPENDS WHAT YOU
CONSIDER MISTAKES.
THAT TOPS EVERYTHING HOW TO TRY
TO AVOID, AND IF IT HAPPENS, WHO
SHOULD BE RESPONSIBLE FOR.
BUT I WOULD SAY IN MANY WAYS IT
CAN BE CONSIDERED LIKE A
DIAGNOSTIC TEST, AND, YOU KNOW,
THE LEGAL SYSTEM HAS WAYS TO
HANDLE THAT.
>> RIGHT, RIGHT.
YEAH, MY QUESTION WHICH IS
RELATED TO THAT IS THAT, YOU
KNOW, HOW DO YOU GO ABOUT
VALIDATING DEEP NEURAL NETWORK
MODELS SO THAT THEY CAN BE USED
FOR DIAGNOSTICS AND OBVIOUSLY IT
HAS IMPLICATIONS FOR ACCURACY AS
WELL.
>> RIGHT.
SO LIKE I MENTIONED, YOU DON'T
NEED TO RETRAIN NECESSARILY WITH
YOUR DATA BUT ONCE YOU HAVE
FIXED MODEL YOU CAN APPLY TO
YOUR DATA, AND SAY -- AND ASSESS
THE PERFORMANCE IN YOUR DATA
BOTH IN TERMS OF DISCRIMINATIVE
PERFORMANCE AND CALIBRATION, SO
THAT SHOULD BE THE MINIMUM THAT
SHOULD BE DONE BEFORE TURNING ON
A PARTICULAR MODEL.
BUT I'M FIRST TO SAY IT'S
PROBABLY NOT DONE.
>> YEAH, YEAH.
ALL RIGHT.
LAST QUESTION IS SO THE OCTOBER
2023 DATA SNAPSHOT FOR ALL OF US
SHOWS A 3% PARTICIPATION RATE
FOR ASIAN PARTICIPANTS VERSUS 16
AND 17% FOR BLACK AND HISPANICS.
DO YOU HAVE ANY INSIGHT INTO THE
LOW PARTICIPATION NUMBER FOR
ASIANS, MORE PRIVACY CONCERNS,
LESS ADVERTISING, ANY INSIGHTS
ABOUT WHY THERE'S SUCH A BIG
DIFFERENCE?
>> WELL, I CAN SAY AS A
RECRUITMENT CENTER WE DO
EVERYTHING WE CAN TO EXPAND AND
NOT FORGET ANY PARTICULAR
POPULATION.
IT DOES TAKE I'M.
I WOULD SAY I CAN CONFIRM THOSE
NUMBERS RIGHT NOW -- CAN'T
CONFIRM THOSE NUMBERS NOW BUT I
WOULD SAY THE NUMBERS IN THE
"ALL OF US" PROGRAM ARE HIGHER
FOR UNDERREPRESENTED
BIOMEDICAL -- IN BIOMEDICAL
RESEARCH POPULATIONS THAN ANY
OTHER COHORT STUDY.
SO THAT'S NOT TO SAY WE DON'T
HAVE A LOT OF IMPROVEMENT TO DO,
BUT IT'S UNPRECEDENTED, THE
REPRESENTATION OF CERTAIN
SEGMENTS.
>> YEAH, YEAH.
BLACKS AND HISPANICS, YOU KNOW,
THOSE NUMBERS ARE BETTER THAN I
WOULD HAVE THOUGHT, BUT ASIANS
BEING SO LOW IS A LITTLE
SURPRISING.
>> WE HAVE WORK TO DO.
>> ONE LAST QUESTION, SINCE THIS
IS THE ADA LOVELACE LECTURE, WE
OFTEN HEAR THERE ARE NOT ENOUGH
WOMEN IN STEM, AND MANY OF US
SAY THAT THERE ARE ENOUGH WOMEN
IN MEDICAL SCHOOLS AND IN
Ph.D. PROGRAMS AND IN
INFORMATICS MAYBE.
WHAT HAVE YOUR EXPERIENCES BEEN
ABOUT THE REPRESENTATION OF
WOMEN IN CLINICAL SCIENCES AND
INFORMATICS, DO YOU THINK
THERE'S STILL A SHORTAGE OF
WOMEN IN THESE AREAS?
IF SO, WHAT CAN WE DO TO ADDRESS
THAT?
>> YEAH, I WOULD SAY STATISTICS
IN MEDICAL SCHOOLS SHOW EQUAL
REPRESENTATION FOR INCOMING
CLASSES, RIGHT?
IF YOU CONSIDER REPRESENTATION
AT THE PROFESSORSHIP LEVEL AND
LEADERSHIP POSITIONS YOU SEE A
DIFFERENT PICTURE.
PROGRESS IS BEING MADE.
I DARE SAY MORE IN MEDICINE THAN
PROBABLY ENGINEERING, BUT A
WHOLE LOT MORE NEEDS TO BE DONE.
AND I THINK WE'RE, YOU KNOW,
INCREASINGLY HAVING MORE ROLE
MODELS, MORE EXAMPLES, AND WE
WILL GET THERE.
HOPEFULLY WE CAN ACCELERATE A
LITTLE BIT THIS PACE, BUT IT HAS
BEEN A PRODUCT OF, YOU KNOW,
UNDERREPRESENTATION AT HIGH
LEVELS.
>> WELL, LET'S HOPE EVERYTHING
KIND OF TRICKLES UP AND WE GET
BROADER REPRESENTATION AT THOSE
HIGHEST LEVELS.
WE'RE AT THE END OF THE HOUR.
LET ME JUST THANK DR.
OHNO-MACHADO AGAIN FOR THE
ENLIGHTENING PRESENTATION.
THE RECORDING WILL BE ARCHIVED
THROUGH THE NIH VIDEOCAST
WEBSITE.
IF YOU HAVE ANY ADDITIONAL
QUESTIONS PLEASE FEEL FREE TO
REACH OUT TO ME, OR TO DR.
OHNO-MACHADO DIRECTLY, AND I'M
SURE SHE WOULD BE HAPPY TO
ANSWER THEM.
THANK YOU, AGAIN, LUCILA.
THANK YOU SO.