>> I WOULD LIKE TO WELCOME
EVERYONE FOR TODAY'S
MORBIDITY WINNER'S WEBINAR,
ALSO A MOUTHFUL NICHD--ENHANCE THE LIVES
OF CHILDREN AND ADOLESCENCE AND OPTIMIZE
ABILITIES FOR ALL.
MATERNAL HEALTH AND A MAJOR RESEARCH
FOLK ISOTOPE FOR OUR INSTITUTE WITH
PARTICULAR EFFORTS FOCUSED ON REDUCING
MATERNAL MORBIDITY AND MORTALITY.
NEARLY A DECADE AGO, NICHD FUNDED THE
NEW MOM TO BE PROJECT.
NEW MOM TO BE STANDED FOR THE
[INDISCERNIBLE] PREGNANCY OUTCOMES STUDY
MONITORING MOTHERS TO BE.
THIS WAS A RACIALLY ETHNICALLY AND
GEOGRAPHICALLY DIVERSE SAMPLE OF 10,000
PEOPLE WHO WERE PREGNANT FOR FIRST TIME.
THIS COHORT WAS VERY RICHLY AND DEEPLY
PHENOTYPES AND IT FOLLOWED THE WOMEN
FROM THEIR SIXTH WEEK OF PREGNANCY
THROUGH THEIR DELIVERIES.
THE PRIMARY OUTCOME WAS TO DETERMINE THE
RISK FOR PRETERM DELIVERY.
BUT WE WONDERED WOULD IT BE POSSIBLE TO
RELIEVE US FROM THE DATA FROM THE STUDY
TO REANALYZE IT FOR RISK FACTORS FOR
SEVERE MATERNAL MORBIDITY AND MORTALITY.
WE DECIDED TO OFFER THIS AS NICHD'S
FIRST EVER DATA CHALLENGE WITH THE GOAL
OF DEVELOPING INNOVATIVE COMPUTATIONAL
METHODS TO ANALYZE THE NEW MOM TO BE
DATA TO DETERMINE RISK FACTORS FOR MA
TERBAL MORBIDITY.
WE RECEIVED A ROBUST RESPONSE WITH
THREIVE SUBMISSIONS AND WE WERE THRILLED
BY THE RANGE OF APPROACHED USED BY THE
WINNING TEAMS, I'M LOOKING FORWARD TO
HEARING ABOUT THEM TODAY.
SOME FOCUSED ON UNIQUE APPROACHES AND
METHODOLOGIES WHILE OTHERS ANALYZE THE
DATA COLLECTION METHODS OF THE NEW MOM
TO BE DATA SET.
OTHER GROUPS USED INNOVATIVE STATISTICAL
EQUATIONS TO FOSTER NEW PREDICTION
MODELINGS FOR CO-MORBIDITIES, SPECIFIC
TO MATERNAL MORBIDITY.
ULTIMATELY WE AWARDED PRIZES 7 PRIZES
FOR FOR INNOVATION AND 5 PRIZES FOR
ADDRESSING HEALTH DISPARITIES.
I WOULD LIKE TO TAKE THIS OPPORTUNITY TO
THANK OUR STAFF AT NICHD AND THE STAFF
AT THE NIH, OFFICE OF DIRECTOR AND THE
NASA CENTERS FOR EXCELLENCE FOR
COLLABORATIVE INNOVATION WHO HELPED
ORGANIZE THIS CHALLENGE, I WOULD ALSO
LIKE TO THANK FREELANCERS OUR CONTRACT
PARTNER WHO HELPED US IMPLEMENT THIS
CHALLENGE.
I WOULD LIKE TO EXPRESS OUR APPRECIATION
TO MANY JUDGES INCLUDING THOSE WHO SERVE
NIH, THE MATERNAL CHILD HEALTH BUREAU OF
THE HEALTH RESOURCES AND SERVICES
ADMINISTRATION AND THE REPRODUCTIVE
HEALTH BRANCH OF THE CENTERS FOR DISEASE
CONTROL AND PREVENTION WHO HELPED US TO
EVALUATE THE APPLICATIONS.
THIS AFTERNOON WE ARE LOOKING FORWARD TO
HEARING FROM OUR WINNING TEAMS TO LEARN
MORE DETAILS ABOUT THEIR DESIGN AND
METHODS, WHAT A LEARNED, AND HOW WE
MIGHT BE ABLE TO USE THESE APPROACHES
FOR FUTURE STUDIES AND,AINAL SIS.
>> I WOULD NOW LIKE TO TURN IT OVER TO
DR. DAVIS TO INTRODUCE OUR PRESENTERS.
>> JUST A FOOT NOTE TO DR. BIANCHI'S
COMMENTS, 1 OF OUR TEAM HAD A CONFLICT
WITH THEIR PRESENTATION SO WE WILL HAVE
6 TODAY.
THE FIRST TEAM THAT'S GOING TO PRESENT
THIS MORNING, THIS AFTERNOON WILL BE THE
TEAM FROM COLUMBIA UNIVERSITY,
DR. [INDISCERNIBLE] WILL REPRESENT THAT
TEAM AND THE TITLE OF HER PRESENTATION
IS PREDICTING AND UNDERSTANDING
PREELAMPSIA, A MACHINE LEARNING
APPROACH.
>> GOOD AFTERNOON, THANK YOU FOR THE
PRESENTATION,IME HERE ON BEHALF OF MY
TEAM AND IT INCLUDES COLLABORATORS FROM
COMPUTER SCIENCE [INDISCERNIBLE] AND
DR. CHEN AND A DOCTOR HO IS AN OBGYN AND
WE ALSO HAVE JOINT WORK WITH ANITA RAJA,
AND HER STUDENTS, ADAM, DANIEL, AND
ALISA.
NEXT SLIDE, PLEASE.
SO IN THIS PRESENTATION, I WILL
INTRODUCE PREECLAMPSIA AND DESCRIBE THE
NICHD CHALLENGE DATA AND THE STUDY
COHORT, I WILL GO OVER OUR PIPELINE TO
BUILD MODELS AND OUR RESULTS IN TERMS OF
PREDICTION, ABILITY AND TOP FEATURES
MPLET THEN WHY TALK ABOUT FAIRNESS CHECK
IN AND MITIGATION IN OUR MODELS.
AND CONCLUDE WITH OUR ONGOING RESEARCH
AND NEXT STEPS.
NEXT SLIDE, PLEASE.
SO PREECLAMPSIA OR PE FOR SHORT IS A
HYPER TENSIVE DISORDER AND 1 OF THE MOST
SERIOUS AND LIFE THREATENING PREGNANCY
OUTCOMES, IT IS CHARACTERIZED BY POOR
PROFUSION IN TISSUE DURING PREGNANCY, PE
RARELY DEVELOP A DANGEROUS COMPLICATION
CALLED ELAMPSIA, A NEW ONSET OF SEIZURES
NOT RELATE TOTD ANY OTHER MEDICAL
REASON, THE TERM ECLAMPSIA ORIGINATES
FROM THE GREEK WORD EKLAMPSIA, FOR
LIGHTNING OR LIGHT BURST.
DESPITE BEING KNOWN FOR CENTURIES
PREECLAMPSIA REMAINS A CHALLENGE FOR
CLINICIANS AND PREGNANT WOMEN.
IN PARTICULAR WE STILL DON'T KNOW THE
ORIGINS OF THE DISEASE OR OF THE AND IS
UNDERLYING TYPES NOW WE KNOW HOW TO
PREDICT, PREVEPT AND TREAT IT.
NEXT, PLEASE.
THE ONSET TEND TO OCCUR AROUND 20 WEEKS
OF GUESTATION TODAY IT IS KNOWN AS A
OBSTETRICAL SYNDROME RATHER THAN SCEEZ
BECAUSE OF ITS COMPLICATIONS, FIRST IS
THOSE RANGING FROM THE NEUROLOGICAL,
RENAL, HEPATIC AND CAUSING SERIOUS
DAMAGE TO BLOOD VESSELS BESIDES MATERNAL
MORBIDITY AND PRENATAL MORBIDITY, THE
RISK ARE ASSOCIATED WITH PREECLAMPSIA.
NOTE THAT MOST DEATHS DUE TO PE OCCUR IN
LOW INCOME COUNTRIES WHERE WOMEN DON'T
HAVE ACCESS TO PROPER HEALTHCARE DURING
PREGNANCY BUT EVEN IN DEVELOPED
COUNTRIES MATERNAL MORTALITY IS VERY LOW
BUT DUE TO PE IT'S VERY LOW.
THERE IS A LARGE BODY OF LITERATURE ON
PE WHICH INCLUDES PRIOR PE, CHRONIC
HYPER TENSION, DIABETES, OBESITY AND
MULTIPLE GUESTATION GUESTATION WHICH ALSO SUPPORTS
DIFFERENT TYPE PREECLAMPSIA.
THE DECODING CHALLENGE WAS DERIVED FROM
THE NULLIP A ROUS PREGNANCY OUTCOMES
STUDY, THIS WAS DONE BETWEEN
OCTOBER 2010 AND MAY 2014.
VARIOUS DATA WAS COLLECTED BY VARIOUS
WOMEN ACROSS 4 TIME POINTS, 3 VISITS
CORRESPONDING ROUGHLY TO THE FIRST,
SECOND AND THIRD TRIMESTER THAT'S VISIT
1, 2, 3, AS WELL AS DELIVERY VISIT.
WE SELECTED AN INCREMENTAL SET OF
FEATURES OR VARIABLES TO REACH 183
FEATURES, AND MANY IN ACCORDANCE WITH
RISK FACTORS, IDENTIFIED IN THE MEDICAL
LITERATURE, ABOUT FOOD NUTRITION,
PHYSIOLOGY, ULTRA SOUND, MEDICAL
HISTORY, SLEEP CONDITION, DEMOGRAPHICS
AND FAMILY MEDICAL HISTORY.
NEXT SLIDE, PLEASE.
SO WE WANT TO BUILD A PREDICTION MODELS,
AND DIFFERENT VISITS FOR ELAMPSIA AND
PREECLAMPSIA, AND DIG INTO OUR MODELS
FOR DRIVING PREDICTION AND THE BEST
THRESHOLD FOR THE RISK OF PE, SO WE
FIRST POSED ABOUT PREVENTIVE DATA, SO
2243 PATIENTS WERE SELECTED TO
PARTICIPATE IN THE STUDY.
SUBJECT WITH CHRONIC HYPER TENSION, MILD
PREELAMPSIA, SUPER IMPOSED PREECLAMPSIA
AND NEW ONSET HYPER TENSION WERE
EXCLUDED.
NEXT, PLEASE.
SO WE WOULD A MACHINE LEARNING PIPELINE
TO CREATE COMPOSITE PRECLICTIVE MODEL
FOR ECLAMPSIA AT STUDY VISITS.
WE CONDUCTED 100 TRIALS, EACH TAKING A
SAMPLE OF TRAINING AND TESTING, 80% FOR
TRAINING AND CROSS VALIDATION AND 20%
FOR TESTING, GIVEN THE IMBALANCE IN THE
DATA SET, WE DON'T SAMPLE THE MAJORITY
CLASS TO BALANCE THE DATA AND ACHIEVE
THE SAME PROPORTION OF CASES TO
CONTROLS.
WE RAN A SEARCH TO BUILD THE BEST
MODELS.
THEY WERE THEN OBTAINED TO THE TEST SET.
WE AVERAGED A RESULT OF THE PREDICTION
SCORES OVER THE TRIALS WE MADE.
NEXT PLEASE.
SO WE FOUND THAT IN SIMPLE METHODS, LIKE
IN BOOSTING, XGBOOST AND RANDOM MODELS
PERFORMED THE BEST, THIS RESULT
INDICATED OUR MODELS ARE GOOD PREDICTERS
ON PREECLAMPSIA RISK AS EARLY AS VISIT 1
AND PREDICTION CAPABILITIES INCREASE
WITH GUESTATIONAL AGE.
WE OBTAINED PRETTY GOOD AUCs AND
SCORES ACROSS THE VISITS.
WHAT MAKES THIS COMPELLING FOR
PREECLAMPSIA AND PROBABLY EXPLAINS THEIR
PERFORMANCE IS THAT BUILDING THE VARIETY
OF MODELS FROM THE SAME COHORT, GIVE A
CHANCE FOR DIFFERENT TYPES TO EMERGE AND
CAPTURE DIFFERENT SUBTYPES OF
PREECLAMPSIA.
NEXT PLEASE.
WE ALSO EXTRACTED THE FEATURED
IMPORTANCE OF LEADING TOP MANUFACTURES
FOR THE LEADING MODELS, THE FEATURES
FOUND TO BE MOST INFORMATIVE FOR VISIT
3, INCLUDE, WEIGHT, RESTING BLOOD
PRESSURE, DIET AND SERUMS BIOMARKERS, IN
ADDITION TO THESE, [INDISCERNIBLE] INDEX
WAS FOUND TO BE IMPORTANT.
THE BIOMARKERS FOUND TO BE INFORMATIVE
ARE [INDISCERNIBLE], VGF, PHG AND ITEM
12 AND [INDISCERNIBLE] WHICH IS
CONNIVITIENT WITH THE LITERATURE.
WE ALSO FOUND THAT COMSUMPTION OF FATTY
ACIDS, SATURATED FAT AND CHOLESTEROL TO
BE PREDICTIVE AS WELL.
NEXT, PLEASE.
WE FURTHER EXPLORED THE PLOTS OR PDPs
TO DETERMINE THE MARGINAL EFFECT OF ANY
GIVEN FEATURE OF THE AVERAGE PREDICTIVE
RESULT ON THE MODEL.
FOR INSTANCE FOR THE RISK FACTOR OF THE
PREGNANCY BMI AND WE SEE INCREASED RISK
OF PE, OF 23.38, ON THE LEFT CURVE AND
110 FOR THE RESTING RESTING RESTING SYST OLIC PRESSURE ON
THE RIGHT.
WE FOUND THIS COULD BE NOR CONSERVATIVE
THAN CURRENT PRACTICE.
NEXT SLIDE, PLEASE.
>> OUR MODELS FOR RACIAL AS WELL FOR
FAIRNESS AND OBSERVED A BIAS MAINLY
AGAINST NONHISPANIC BLACK WOMEN AS SHOWN
HERE IN THE RED BAR.
USING THE WHITE RACE AS A REFERENCE
RACE, WE IDENTIFIED THAT THE PREDICTIVE
REACH OF A BLACK WOMEN WAS 1.314 WHICH
IS HIGH ACCORDING TO THE FIFTH FIGHT
RIEWFUL.
IN OTHER WORDS OUR MODELS FALSE-POSITIVE
RATE WAS HIGHER IN THE BLACK POPULATION
WHICH IS PROBLEMATIC SINCE PATIENTS WITH
NO RISK OF PE IN THE SUBPOPULATION WOULD
BE SUBJECTED AND THIS IS VERY--THIS
TESTS TREATMENT AND POTENTIAL TERM
NATION OF PREGNANCY IF THIS MODEL WERE
TO BE USED.
NEXT, PLEASE.
ONE WAY TO CORRECT FOR THE BIAS IS TO
DETERMINE THE BEST PREDICTION THRESHOLD
FOR THE AFRICAN AMERICAN POPULATION TO
ACHIEVE THIS WE PLOTTED SUCH A PLOT OF
THE SUBPOPULATION TO DETERMINE THE
SELECTION CRITERIA.
NEXT PLEASE.
BY SLIGHTING INCREASING THE THRESHOLD WE
WERE ABLE TO CHOOSE THE PREDICTIVE
QUALITY RATIO, FOR AFRICAN AMERICAN
MEANT 1.114 AS SHOWN IN THE FIGURE WITH
THE [INDISCERNIBLE].
TO SUMMARIZE OUR WORK, AND OUR
EXPERIMENTS, OUR WORK SHOWS THAT IT IS
POSSIBLE TO CREATE A COMPOSITE SCREENING
MODEL TO PREDICT THE WOMAN AT RISK OF
DEVELOPING SEVERE PREELAMPSIA AND
PREELAMPSIA AT 13 WEEKS GUESTATION, THE
LINES ARE IN LINE WITH THE LITERATURE.
THEY STRESS THE IMPORTANCE OF USING
SEVERAL TESTS AND USING THE INDEX, WE
INCLUDED IT TO INCLUDE ANY SOCIOECONOMIC
OR RACIAL DISPARITY IN THE MODEL AND
PROCESS TO METIGATE THEM.
FINALLY THE PROPOSED WORK COULD BE
EASILY ADAPTED TO ADDRESS ALL THE
MATERNAL MORBIDITYS AND APORKSs, NEXT
SLIDE, PLEASE.
OUR TEAM HAS BEEN WORKING ON THE
CHALLENGE AND THIS OPENED A NEW RESEARCH
OPPORTUNITY THAT WE WOULD LIKE TO
PURSUE.
CURRENTLY, WE ARE PREPARING PUBLICATION
TO SHOW RESULTS, WE ARE ALSO BUILDING
MODELS TO COMPARE EARLY, FOR BEFORE 34
WEEKS AND LATE ONSET OF PE, AFTER 34
WEEKS AND WE ALSO WILL RUN OTHER TESTS
FOR THE THRESHOLD MODELS, HOWEVER,
FURTHER RESEARCH IS USED TO DEFINE TO
FIND CAUSES OF THE BIAS AND ALSO
IDENTIFYING PREECLAMPSIA BITES WITH THE
MACHINE LEARNING AND INTERPRETABLE
MODELS, I WOULD LIKE TO THANK
DR. MAURICE AND THE CHALLENGE TEAM FOR
THIS EXCITING CHALLENGE AND THANK ALL OF
OUR COLLABORATORS FROM COLUMBIA AND ALL
OF OUR STUDENTS.
THANK YOU.
>> THANK YOU DR. SALEB.
WE DO HAVE TIME FOR QUESTIONS SO WE'RE
GOING TO TAKE QUESTIONS AFTER EACH
RESENTATION.
SO WE DO HAVE A FEW MINUTES.
SO AND I WANT TO REMIND ALL PARTICIPANTS
THAT IF YOU WOULD LIKE TO ASK A
QUESTION, PLEASE USE THE QUESTION AND
ANSWER BUTTON AT THE BOTTOM OF YOUR
SCREEN.
THERE IS 1 QUESTION THAT I WOULD LIKE TO
ASK AND THAT IS HOW IS THE 4-FIFTH RULE
APPLIED IN THE CONTEXT OF FAIRNESS CHECK
NOTHING YOUR APPLICATION.
>> THANK YOU.
THIS IS A GREAT QUESTION.
SO FROM THE 4-FIFTHS RULE, IT'S APPLIED
IN THE PROCESS AND THINGS LIKE THAT, IN
OUR CONTEXT FOR THE UNDERPRIVILEGED
GROUP WHICH IS THE AFRICAN WOMEN IN THE
DATA WHEN THEY RECEIVE THE FIRST
POSITIVE RATE OUTCOME, THAT'S BECAUSE
THE GROUP IS MORE THAN 20%, SO THAT'S
PARTICULARLY FOR THE GROUP TO REPRESENT
IF THE--IF THE RATE OF THE
FALSE-POSITIVE RATE IS BIGGER BY 20% AND
THE RATE OF THE WHITE CAUCASIAN FEMALE
IN THE DATA THEN WE DISCOVER THE
DISPARITY AND THAT'S WHAT WE WANT TO
BRING IT DOWN TO LESS THAN BASICALLY
20%.
AFTER THE MITIGATION, WE REDUCED THE
DISPARITY FROM 1.31 TO 1.2 WHICH IS MORE
SUSCEPTIBLE AND FILLS THE RULE OF THE
4-FIFTHS RULE.
THANK YOU.
>> GREAT, THANK YOU.
THERE IS 1 MORE QUESTION THAT I JUST
RECEIVED FROM A PANELISTS, I WOULD LIKE
TO ASK YOU DID YOU COMPARE THE
PERFORMANCE OF DOWN SAMPLING VERSUS
UPSAMPLING FOR SYNTHETIC DATA SUCH AS
SMOOT?
>> THIS IS A GOOD QUESTION.
WE TRIED SMOOT TO SAMPLE THE MINORITY
CLASS AND WE ALSO TRIED ENDOSAMPLING, SO
WE HAD SLIGHTY BETTER PERPERFORMANCE
WITH UNDERSAMPLING AND IT WAS FASTER TO
TRAIN THE MODELS THAN USING
OVERSAMPLING.
SOPHISTICATEDY WE FOUND BETTER AREY
SULTS WITH THE SMALL MARGIN VERY RESULTS
WITH UNDERSAMPLING, SO YES, THIS WOULD
BE WAY OR THE OTHERS TO ACCOMPLISH THE
BALANCING OF THE DATA SET.
>> AND I THINK WE HAVE TIME FOR 1 MORE
QUESTION, DID YOU TRY OTHER BAGGING
METHODS SUCH AS RANDOM FORCE,
CLASSIFIERS VERSUS BOOSTING METHODS?
>> THIS IS A GREAT QUESTION.
QUESTION YES WE TRIED BOTH, EXTRA BOOST
WHICH IS A BOOSTING METHOD, SO THERE ARE
BOTH SAMPLES, THEY OFFER A BIT
DIFFERENTLY SO FOR THE STUDY FOR US IT
DEMONSTRATED THAT YOU MAY VOTE FOR THE
MODEL AND IMPROVE IT BY DOING SOME
[INDISCERNIBLE].
AND WE TRIED BOTH AND WE SAW CLOSE
PERFORMANCE BETWEEN RANDOM FORCE AND
EXTRA BOOSTING, GREAT, QUESTION, THANK
YOU.
>> THANK YOU DR. SALLEB, IF WE HAVE TIME
AT THE END, I MAY ASK SOME ADDITIONAL
QUESTIONS BUT FOR RIGHT NOW, SINCE WE
HAVE MORE PRESENTATIONS I THINK WE NEED
TO MOVE FORWARD JUST A LITTLE BIT.
BUT THANK YOU VERY MUCH FOR YOUR
PRESENTATION AND FOR YOUR WORK.
>> OUR NEXT PRESENTER IS MISS BRITTANY
JOHNSTON FROM THE JOHN TON COMPANY OUT
OF SALT LAKE CITY, UTAH AND THE TITLE OF
HER PRESENTATION IS: THE RELATIONSHIP
BETWEEN MARGINALIZING BEHAVIORS AND
POSTPARTUM COMPLICATIONS FOR NULLIP A
ROUS WOMEN RECEIVING AN UNDESIRED
C-SECTION.
BRITNEE?
>> HI, THANK YOU.
>> YOU'RE ON, THANK YOU.
>> HI, EVERYONE I'M BRITNEE JOHNSTON.
THANK YOU FOR HAVING ME TODAY, I'M
EXCITED TO SHARE A SUMMARY OF MY DATA
PROPOSAL WITH YOU ALL.
LET'S GO TO THE NEXT SLIDE.
SO IN MY PROPOSAL MY HYPOTHESIS WAS THAT
A CORCALCULATIONS EXISTS BETWEEN
MARGINALIZING BEHAVIORS AND POSTPARTUM
COMPLICATIONS, SPECIFICALLY WITH AN
ENULLIPPOR OUS DATA SET.
--IF THEY SPEAK UP IF THEY'RE TREATED
UNFAIRLY AND IF THEY HAD LOW
SOCIOECONOMIC STAT US.
NEXT SLIDE.
SO I WANT TO PROVIDE DATA HOW I CAME UP
WITH THE IDEA.
SO IN THE NICHD, THERE ARE THOUSANDS OF
ROWS OF POSSIBLE VARIABLES DATA THAT WE
COULD EXPLORE AND 1 THAT STOOD OUT TO ME
IF SOMEONE EVER EXPERIENCED DISCROSS
EXAMINE NATION IN A HEALTHCARE SETTING,
I THOUGHT IT WAS INTERESTING.
SO I LOOKEDDA THE PAST RESEARCH THAT
NICHD HAD CONDUCTEDUTESSING THE NEW MOM
TO BE DATA SET AND I SAW THAT
DISCRIMINATION HAD NOT BEEN EXPLORED
BEFORE SO I THOUGHT THIS MIGHT BE AN
INNOVATIVE WAY TO LOOK AT THE DATA.
I TOOK IT 1 STEP FURTHER BY NARROWING
THE GROUP I WOULD LIKE AT TO WOMEN WHO
RECEIVED C-SECTIONS AND SO IN MY BRIEF
LITERATURE REVIEW, I SAW THAT C-SECTIONS
WERE KNOWN TO BE AN UNNECESSARY RISK IF
IT WAS NOT MEDICALLY NEEDED AND THEY
COULD BE MORE RISKY THAN VAGINAL
DELIVERIES.
YET THE RATES OF C-SECTIONS HAVE
INCREASED FROM 7% IN 1990 TO 21% IN
2021.
SO IT IS BECOMING A MORE COMMON
PROCEDURE.
I ALSO FOUND THAT C-SECTIONS THEIR RATES
WERE HIGHER FOR BLACK WOMEN N. A
CALIFORNIA HEALTH REPORT, THEY WERE
QUESTIONING WHETHER BLACK WOMEN WERE
BEING LISTENED TO AND INCLUDED IN THE
TEAM FOR MAKING DECISIONS FOR THEIR
HEALTH.
SO THAT MADE ME QUESTION IF THERE WAS A
LINK BETWEEN BEING DISCRIMINATED AGAINST
AND NOT FAIRLY LISTENED TO AND MAKING A
DECISION IN THE DELIVERY ROOM AND
LEADING TO A C-SECTION AND TO A
COMPLICATION AFTER THE FACT.
SO THAT'S WHERE MY INITIAL THINKING CAME
FROM AND HOW I CAME UP WITH THIS
RESEARCH PROPOSAL.
NEXT SLIDE.
SO FOR THE DATA AND SAMPLE, I USE THE
NEW MOM TO BE DATA SET THAT WAS PROVIDED
TO US IN DATA CHALLENGE.
I CREATED A SAMPLE GROUP OF JUST OVER
1100 WOMEN WHO-FIC WANTED A VAGINAL
DELIVERY BUT RECEIVED A C-SECTION
INSTEAD AND COMPARISON WITH SUMMARY
STATISTICS WITH JUST OVER 3600 WOMEN WHO
RECEIVED THEIR DESIRED VAGINAL DELIVERY
SO I DIDN'T USE JUST A 1 OUTCOME RESULT
VARIABLE THAT THEY RECEIVED A C-SECTION,
I WANT TO MAKE SURE IT REFLECTED THAT A
DECISION HAD TO HAVE BEEN MADE TO CHANGE
THEIR MIND TO END UP WITH A C-SECTION
INSTEAD AND THAT IT WASN'T AN ELECTIVE
DECISION OR PREPLANNED.
NEXT SLIDE.
SO FOR MY METHOD, I USED A REGRESSION
MODEL USING R, MY DICHOT MOUSE DEPENDENT
VARIABLE WAS AN INSTANCE OF POSTPARTUM
COMPLICATION, THAT INCLUDED HEMORRHAGE,
RETAINED PLACENTA, WOUND INFECTION AND
SO ON.
FOR MY INDEPENDENT VARIABLES, THOSE
INCLUDED AGE, RACE AND ETHNICITY,
WHETHER THEY WERE MARRIED, THEIR
EDUCATION LEVEL, IF THEY--THEY WERE 200%
BELOW THE POVERTY LEVEL, THEY INCLUDED
HEALTH RELATED VARIABLES INCLUDING IF
THEY WERE BORN BY VAGINAL DELIVERY
THEMSELVES WHEN THEY WERE A BABY AND
ALSO MEDICAL REASONS FOR THEIR
C-SECTION.
THAT INCLUDED IF THE BABY WAS TOO BIG OR
CERVIX WAS NOT DILATING AND ALSO
INCLUDED IF THE WOMAN DIDN'T KNOW HER
REASON FOR HER C-SECTION.
NEXT SLIDE.
SO CONTINUING ON MY METHOD, I CHECKED
FOR CLASSIFICATION BIAS AND I FOUND THAT
THE PROPORTION OF INSTANCES AND
NONINSTANCES OF POSTPARTUM COMPLICATIONS
WAS NOT THE SAME.
SO I TESTED SAMPLING METHODS TO MODIFY
THE IMBALANCED DATA TO A BALANCED
DISTRIBUTION.
SO I TESTED UNDER SAMPLING, AND
OVERCHAMPLING AND BOTH UNDER AND OVER
SAMPLING, AND DATA GENERATION, SO OF
THOSE TESTS, OVERSAMPLING WAS THE BEST
MODEL FOR ACCURACY WITH AN AUCOF 0.74.
AND THEN I TOOK AN ADDITIONAL STEP TO
GROUP THOSE TOGETHER BY RACE ASK
ETHNICITY FOR STATISTICS TO SEE IF THERE
WERE TREPPEDS THERE AS WELL.
NEXT SLIDE.
OKAY, SO ON TO THE FINDINGS, SO I WILL
SHARE 4 OF THE MAIN FINDINGS FROM MY
PROPOSAL, THE FIRST IS THAT C-SECTIONS
HAD A HIGHER RATE OF POSTPARTUM
COMPLICATIONS AT 9% COMPARED TO THOSE
WHO RECEIVED VAGINAL DELIVERY AT 4%.
BY RACE AND ETHNICITY, BLACK WOMEN WITH
C-SECTIONS HAD THE HIGHEST RATEST
POSTPARTUM COMPLICATIONS AT 15% FOLLOWED
BY ASIAN WOMEN AT 14%.
SO THIS MATCHED WHAT I HAD SEEN IN MY
LITERATURE REVIEW.
NEXT SLIDE.
DISCRIMINATION HAD A HIGHER LIKELIHOOD
OF POSTPARTUM COMPLICATIONS SO THE ODDS
OF HAVING A POSTPARTUM COMPLICATION WAS
2.7 TIMES HIGHER IF THE WOMAN
EXPERIENCED DISCRIMINATION IN HEALTHCARE
SETTING COMPARED TO THOSE WHO DID NOT.
AS FAR RACE AND ETHNICITY, BLACK WOMEN
HAD THE HIGHEST RATES OF EXPERIENCING
DISCRIMINATION AT 6%, AND KEEPING TO
THEMSELVES IF TREATED UNFAIRLY AT 15%.
NEXT SLIDE.
SOCIOECONOMIC STATUS PLAYED A PART AS
WELL, SO IN MY MODEL THE ODDS OF HAVING
A POSTPARTUM COMPLICATION WERE 69%
HIGHER IF THE WOMAN WAS LOW INCOME.
IT WAS 49% LOWER IF THE WOMEN WAS
MARRIED AND 33% LOWER IF THE WOMEN HAD A
HIGH SCHOOL EDUCATION OR LESS.
AND THEN AGAIN LOOKING AT DESCRIPTIVE
STATISTICS, BLACK WOMEN HAD THE HIGHEST
RATES OUT OF RACIAL/ETHNIC GROUPS FOR
BEING LOW INCOME, BEING UNMARRIED AT 80%
AND HAVING A HIGH SCHOOL EDUCATION OR
LESS.
NEXT SLIDE.
I ALSO LOOKED AT THE MEDICAL REASONS FOR
C-SECTION AND THEY HAD A HIGHER
LIKELIHOOD OF THE POSTPARTUM
COMPLICATION, SO OUT OF THE 8 VARIABLES
RELATED TO MEDICAL REASONS, 3 OF THEM
WERE STATISTICALLY SIGNIFICANT, SO THE
BABY WAS TOO BIG, IF THE C-SECTION
REASON WAS UNKNOWN TO THE MOTHER AND IF
THEY WERE EXPERIENCING CHORIOAMNIONITIS.
HISPANICS HAD THE HIGHEST HIGHEST RATES O F
BABIES BEING TOO BIG, AND THEN ALSO
ASIANS HAD THE HIGHEST RATES OF NOT
KNOWING THEIR C-SECTION REASON.
OVER ALL THE MEDICAL REASONS FOR
C-SECTIONS WERE NOT HIGH FOR BLACK WOMEN
BUT THE RATES WERE HIGH IN ALL OTHERS WE
WERE TESTING, THIS FURTHER SUGGESTS THAT
BLACK WOMEN'S POSTPARTUM COMPLICATIONS
MAY BE DRIVEN MORE BY MARGINALIZING
BEHAVIORS AND SOCIOECONOMIC STATUS
RATHER THAT PHYSICAL HEALTH ISSUES.
NEXT SLIDE, MY PROPOSAL SHARED A FEW
FUTURE RESEARCH RECOMMENDATIONS
INCLUDING ADDRESSING DISCRIMINATION IN
HEALTHCARE.
FACILITIES THAT PROVIDE DISCRIMINATION
TRAINING TO THEIR MEDICAL STAFF, MIGHT
HELP MITIGATE THESE OUTCOMES.
SO THE FUTURE RESEARCH COULD LOOK AT
FACILITIES THAT HAVE IMP LIAISON
ELEMENTED DISCRIMINATION TRAINING AND
COMPARED TO THOSE THAT HAVEN'T AND SEE
IF THERE'S A DIFFERENCE IN POSTPARTUM
OUTCOMES, NEXT SLIDE.
FUTURE RESEARCH COULD ALSO FOCUS ON HOW
SINGLE WOMEN ARE SUPPORTED DURING THE
DELIVERY, SO LIKE I MENTIONED THE ODDS
OF A POSTPARTUM COMPLICATION ARE 49%
LOWER PER THOSE WHO ARE MARRIED HOWEVER
ONLY 20% OF BLACK WOMEN WERE MARRIED.
SO FUTURE RESEARCH COULD LOOK AT
PROGRAMS THAT PROVIDE SUPPORT ARE TO
SINGLE WOMEN DURING DELIVERY TO
DETERMINE IF THERE ARE ANY DIFFERENCES
IN THOSE OUTCOMES.
NEXT SLIDE.
AND THEN JUST A FEW OTHER SUGGESTIONS
INCLUDED THAT FUTURE EFFORTS COULD FOCUS
ON COLLECTING MORE DATA ON AMERICAN
INDIAN WOMEN AND THEIR C-SECTION
OUTCOMES.
I DIDN'T HAVE ENOUGH DATAOT SUBGROUP TO
BE ABLE TO REPORT ON THEM ON THE
PROPOSAL.
ALSO HEALTHCARE FACILITIES COULD WORK ON
IMPROVING COMMUNICATION BETWEEN THE
STAFF AND PREGNANT WOMEN, THIS MAY BE
RELATED TO A LACK OF COMMUNICATION IN
GENERAL FOR THOSE WHO DO NOT KNOW THE
REASON OF THEIR C-SECTION AND MAYBE
THEY'RE ALSO NOT BEING COMMUNICATED TO
ABOUT HOW TO TAKE GOOD CARE OF THEIR
C-SECTION LEARNED TO AVOID INFECTION
AFTER THE FACT.
LASTLY MEDICAL PROFESSIONALS THAT HELP
PATIENTS FOCUS ON HEALTHY HABITS DURING
THE PREGNANCY.
WOULD BE MOST HELPFUL FOR HISPANIC WOMEN
SINCE THEY HAD THE HIGHEST RATES OF
MEDICAL REASONS FOR A C-SECTION.
>> ONE MINUTE.
>> NEXT SLIDE.
SO IN CONCLUSION, AS MORE RESEARCH
FOCUSES ON MARGINALIZING BEHAVIORS AND
REVEALING THOSE TRENDS, IT WOULD PROVIDE
MORE MOTIVATION TO HEALTHCARE FACILITIES
TO IMPROVE PRACTICES AND TRAINING TO
HELP REDUCE DISCRIMINATION AND BIAS AND
POSSIBLY LEAD TO REDUCED POSTPARTUM
COMPLICATIONS.
NEXT SLIDE.
SO FEEL FREE CONTACT ME IF HAVE YOU ANY
QUESTIONS ABOUT MY DATA PROPOSAL.
MY INFORMATION IS HERE AND I WANT TO
WRAP UP BY SAYING THANK TO YOU NICHD FOR
THE OPPORTUNITY TO PARTICIPATE IN THIS
DATA CHALLENGE.
IT'S BEEN A GREAT EXPERIENCE AND THANK
TO YOU EVERYONE FOR YOUR TIME TODAY TO
KIND OF LEARN MORE ABOUT OUR PROPOSALS.
SO THANK YOU.
>> THANK YOU VERY MUCH FOR A VERY
INFORMATIVE PRESENTATION.
I DO HAVE A COUPLE OF QUESTIONS IN THE
CHAT FOR YOU.
ONE QUESTION IN PARTICULAR, SAID THANK
YOU FOR THIS INTERESTING TALK.
WERE YOU ABLE TO EVALUATE POTENTIAL
EFFECTS OF ACTIVITY SUCH AS IMMIGRATION
STATUS AND PRIMARY SPOKEN LANGUAGE?
>> YEAH, THAT'S A GOOD QUESTION AND A
GOOD IDEA.
THOSE WERE INCLUDED IN MY MODEL.
WHOOPS--SORRY, THAT MIGHT BE ME.
[BEEPING ], THOSE WERE NOT IN MY MODEL
BUT WOULD HAVE BEEN INTERESTING TO
INCLUDE AND I DON'T REMEMBER OFF THE TOP
OF MY HEAD IF IMMIGRANT STATUS AND THE
PRIMARY LANGUAGE SPOKEN WAS INCLUDE
INDEED THAT DATA SET WE'RE GIVEN.
IT WOULD BE INTERESTING TO LOOK AT.
>> GREAT.
LET'S SEE I THINK THERE--OKAY, I THINK
THAT'S IT FOR THE QUESTIONS.
THANK YOU VERY MUCH AGAIN FOR THAT
PRESENTATION AND LIKE I SAID EARLIER IF
WE HAVE TIME AND ADDITIONAL QUESTIONS
COME UP AT THE END, WE WILL REACH OUT
AND ASK YOU THOSE QUESTIONS THEN.
THANK YOU VERY MUCH FOR THAT GREAT TALK.
>> THANK YOU.
>> OKAY, OUR NEXT PRESENTATION IS FROM
THE IBM DATA SCIENCE AND AI ELITE TEAM.
MR. ANISH [INDISCERNIBLE] WILL BE THE
PRESENTER FOR THIS TEAM AND THE TITLE OF
HIS PRESENTATION IS TRACKING CHANGES IN
HEALTH METRICS BETWEEN VISITS TO MODEL
ADVERSE PREGNANCY OUTCOME AMONG NULLIP A
ROUS WOMEN.
I'M SORRY, I MESSED YOUR NAME UP.
>> DON'T WORRY ABOUT IT.
[LAUGHTER]
>> THANK YOU.
>> I CAN TAKE IT FROM HERE, HELLO
EVERYONE I'M AINESH P A NDEY, AND I
SERVE AS THE IBM LEAD DATA TECHNICIAN
FOR THIS CHALLENGE, I WROTE THE SOLUTION
FOR GABRIEL [INDISCERNIBLE] AND
[INDISCERNIBLE] WERE ALSO ON THE PROGRAM
AS WELL.
WE DECIDED TO--ESSENTIALLY WE BELIEVE
THAT RELATIVE CHANGES IN MOZ
MEASUREMENTS AND NOT THE RAW
MEASUREMENTS THEMSELVES COULD PREDICT
THE DEVELOPMENT OF APOsALATENER
PREGNANCY.
BECAUSE OF THE INPUTS TO OUR APPROACH
WERE PRIMARILY ENGINEERING FEATURES WE
HAD TO BE CAREFUL IN OUR INTERPRETATION,
WE DECIDED TO CALCULATE DELTAS BETWEEN
Z-SCORED MEASUREMENTS SO THE DELTA
REPRESENT CHANGE BISE SPLIEWKS
POPULATION FOR EXAMPLE, AN INCREASE IN
WEIGHT OF 20-POUNDS DURING PREGNANCY,
MEANS MUCH MORE FOR SOMEONE STARTING AT
A LOWER BASE WEIGHT THAN SOMEONE
STARTING AT A HIGHER BASE WEIGHT.
THE DATA SET TOOK THESE CLINICAL
MEASUREMENTS AT 4 STANDARD DOCTORS
VISITS AS OUTLINED ON THE SLIDE.
WE TOOK DELTAS BETWEEN VISITS 1 AND 2, 2
AND 3, AND 1 AND 3.
BECAUSE WE'RE INTERESTED IN IDENTIFYING
APOs EARLY EARLY IN THE PREGNANCY AND
NOT AT TERM, WE DID NOT GENERATE DALTON
WITHERSPOONAS WITH THE FINAL VISIT.
NEXT SLIDE.
THERE ARE 3 CATEGORIES OF DATA WE CARED
ABOUT FOR OUR ANALYSIS.
I ALREADY TALKED ABOUT THE DELTAS WHICH
INCLUDED CLINICAL MEASUREMENTS, SLEEP
MONITORING DATA, INFORMATION ABOUT FETAL
DEVELOPMENT, ET CETERA, THE SECOND
CATEGORY THAT WE NEED TO INCLUDE IN OUR
ANALYSIS WAS CO VARIANTS AND STATISTICAL
MODELS LIKE THE 1S WE CREATE IT IS
IMPORTANT TO CORRECT FOR CERTAIN
VARIABLES.
THE CO VALID AND RELIABLEIATES WITHIN
OUR ANALYSIS WERE THE EGA, SINCE EACH
VISIT FELL IN A RANGE OF LENGTH OF
PREGNANCY.
DEMOGRAPHIC DATA, HEALTH RELATED DAILY
BASIS THEA AND SOCIOECONOMIC DATA.
THE LAST CATEGORY OF COURSE WAS TARGET
VARIABLES FOR ANALYSIS.
WE WANTED TO SEE HOW ACCURATELY, WE
COULD PREDICT PREGNANCY OUTCOMES AND THE
DEVELOPMENT OF POSTPARTUM COMPLICATIONS
OR MENTAL HEALTH CONDITIONS.
FOR THIS ANALYSIS WE SELECTED DIFFERENT
APOs AS TARGETS.
NEXT SLIDE.
NOW THAT WE HAVE THE MODELING DATA SET
DEFINED, WHAT WITH ALL THE DAILY BASIS
THEA CLEANING AND FEATURED ENGINEERING
TO ASCERTAIN DATA QUALITY, WE CAN
DISCUSS THE MODELING METHODOLOGIES THAT
WE INPUT.
NEXT SLIDE.
--THROUGH ALL OF THAT I LEARNED THE
MEDICAL RESEARCH COMMUNITY RELIES
HEAVILY ON PROGRESSION TECH NEEGS
BECAUSE THEY'RE EASY TO TRAIN, SIMPLE
AND INTERPRETABLE, HOWEVER OUR
CONTENTION IS THAT THERE'S MUCH MORE
VALUE IN THE WORLD OF MACHINE LEARNING
FELT OTHER METHODS WHICH ARE ONSEMBLING
OR BOOSTING METHODS ARE BETTER SUITED TO
MODEL COMPLEX RELATIONSHIPS AND OFTEN
POWER BETTER.
NEXT SLIDE.
ENSEMBLE METHODS TAKE BOTH MODELS, A
BOOT STRAP SAMPLE OF THE DATA FOR EACH
MODEL AND ALLOWING IT TO SPECIALIZE
DIFFERENT PARTS OF THE FEATURE SPACE, IN
FACTIS WE SEE THAT ENSEMBLE MODELS
GENERALLY OUTPERFORM SINGLE MODELS.
NEXT SLIDE.
BOOSTING TAKES A SPECIALIZATION THAT
ENSEMBLE BRINGS FOR A WHOLE NEW MODEL
AND IT RELYS ON LEARNERS FOR LARGE SWATH
OF THE INPUT BASE, EACH LEARNER CAN BE
ON THE FEATURE SPACE AT THE PREVIOUS
LEARNER THAT WE PERFORM POORLY ON.
FOR EACH OF OUR 18 TARGETS WE TRAINED TO
THE LOGISTIC PROGRESSION MODEL, RANDOM
MODEL AND LIGHT GBM MODEL TO SEE WHICH
PERFORMED THE BEST.
WE ELIMINATED 14 OF THE 18 APOs FOR
LOW OR NEGATIVE CASES OR UNDER
PERFORMING MODELS AND THE LIGHT GBM
MODELS SHOWED THE BEST RESULTS.
NEXT SLIDE.
NOW WE WILL MOVE ON TO THE RESULTS OF
OUR ANALYSIS.
THE MAIN CHARTER OF THIS CHALLENGE WAS
TO IDENTIFY AREAS OF RESEARCH INTO
APOs WHILE ADDRESSING RACIAL
DISPARITIES.
HOWEVER, WE THINK THAT OUR ANALYSIS ALSO
PAVES THE WAY FOR MORE INDEED SOLUTION
THAT CAN IMPROVE THE LIVES OF PREGNANT
WOMEN TODAY.
NEXT SLIDE.
TO GUIDE FUTURE RESEARCH INTO MATERNAL
MORBIDITY.
THE MOST IMPORTANT RESULT OF OUR
ANALYSIS IS THAT FEATURED IMPORTANCES IS
ASSOCIATED WITH EACH OF OUR MODELS, EACH
HIGHLY IMPACTFUL FEATURE POTENTIALLY
REPRESENTS A PATH FOR FUTURE MEDICAL
RESEARCH.
FOR EXAMPLE, WE FIND THAT THE CHANGE CAN
WAIT BETWEEN VISIT IT IS 1, 2, AND 3, IS
HIGHLY PREDICTIVE OF BOTH POART PART
UMKC DEPRESSION AND ANXIOUS.
THIS INDICATES THAT A BLINDED STUDY
RELATING WEIGHT GAIN THROUGH
PROGRESSINANCE SETHE DEVELOPMENT OF
THESE POSTPARTUM MENTAL ILLNESSES IS
LIKELY PRODUCE A STATISTICALLY
SIGNIFICANT RELATIONSHIP BETWEEN THE 2.
SIMILARLY, THE RESULTS OF OUR ANALYSIS
SHOULD PROVIDE THE BASIS OF SEVERAL
OTHERS EXPLAINED MEDICAL STUDIES.
NEXT SLIDE.
ANOTHER CHARTER OF THIS CHALLENGE WAS TO
ADDRESS HOW IT SPECIFICALLY IMPACTS
FACTIONS OF PREGNANT POPULATION,
THEREFORE FOR EACH IMPACTFUL FEATURE
IDENTIFIED BY OUR FEATURE IMPORTANCES WE
WANTED TO SEE HOW THE DISTRIBUTION CROSS
RACE, THERE'S A CHART MISSING FROM THIS
SLIDE BUT IN THE CHART WE WOULD SHOW
THAT THE RESTING DIASTOOL DNAIC BLOOD
PRESSURE BETWEEN VISIT 2 AND 3, IS
PREDICTIVE OF DEVELOPMENT OF CHRONIC
HYPER TENSION.
THE MAJORITY CLASS, WHICH IS WHITE
FEMALES, SHOWS BEHAVIOR CLOSER TO ME,
HOWEVER SOME RACIAL MINORITIES DISPLAY
GENERAL BEHAVIOR, VERY DIFFERENT FROM
THE NORM.
IN THIS CASE, AMERICAN INDIAN AND ALASKA
NATIVE SHOW A GENERAL--SHOWED A GENERAL
DOWNWARD TREND IN RESTING DIASTOOL DNAIC
BLOOD PRESSURE THROUGHOUT THEIR
PREGNANCY WHILE NATIVE HAWAIIAN AND
OTHER PACIFIC ISLANDERS SHOWED THE
OPPOSITE.
THESE DIFFERENCES IN RACIAL BEHAVIOR CAN
PROVIDE A LENS IN THOSE BLINDED STUDIES
MOVING FORWARD, ALLOWINGITOUS UNDERSTAND
NOT ONLY HOW CERTAIN PREGNANCIES PREDICT
APOs BUT HOW DIFFERENT FACTIONS OF THE
FEMALE POPULATION MAY BE MORE OR LESS
ADVERSELY AFFECTED.
THIS LIST REPRESENTS THE MOST CRITICAL
FINDINGS FROM OUR ANALYSIS, WHO
IDENTIFIED DELTA FEATURES, WHICH
MORBIDITYS THEY IMPACT AND HOW THE
MAJORITY BEHAVES AND WHICH ARE ADVERSELY
AFFECTED.
EACH ROW REPRESENTS A POTENTIAL BLINDED
STUDY TO ASCERTAIN OUR FINDINGS.
NEXT SLIDE.
THE MOST EXCITING ASPECT OF OUR ANALYSIS
HOWEVER, GOES BEYOND THE CHARTER OF THIS
CHALLENGE, WE BELIEVE THAT OUR WORK
OPENS THE PATH TO A MORE IMMEDIATE
SOLUTION THAT CAN START IMPROVING THE
LIVES OF PREGNANT WOMEN NOW INSTUD OF
THROUGH FUTURE RESEARCH.
THE HIGH PERFORMANCE OF OUR MODELS
INDICATES WE CAN DEVELOP REALTIME TOOL
FOR DOCTORS TO USE IN THE EXAM NATION
ROOM TO PROACTIVELY IDENTIFY MORBIDITY
RISK.
ONCE THE SCREENING DATA IS COLLECTED OUR
TOOL CAN PRODUCE A RISK PROFILE WITHIN
SECONDS WARNING TD DOCTOR OF ANY APO
THAT THE PATIENT MAY SHOW SIGNS OF
DEVELOPING.
IN REALTIME, DOCTORS WILL BE ABLE TO
VERIFY USING THEIR OWN EXPERTISE AND
RECOMMEND ALLEVIATING MEASURES BEFORE
THE PATIENT EVEN LEAVES THE HOSPITAL.
FOR EXAMPLE, IF THE RISK PROFILE
INDICATES A HIGH CHANCE OF DEVELOPING
POSTPARTUM DEPRESSION, THE DOCTOR MAY
FOCUS ON PARTS OF THE EXAM NATION
RELATED TO MENTAL HEALTH AND DETERMINE
THE VALIDITY OF THE RISK, IF THE DOCTOR
CONCURRINGS, THEY CAN ADDRESS THIS
CONCERN BEFORE IT ACTUALLY DEVELOPS.
SUCH PREVENTATIVE CARE MAY GO A LONG WAY
TO IMPROVE THE HEALTH OF PERSPECTIVE
MOTHERS AND THEIR CHILDREN.
NEXT SLIDE.
WE ARE ALL VERY EXCITED ABOUT THE WERE
AND REAL WORLD APPLICATIONS OF OUR
ANALYSIS.
YOU CAN FOLLOW THE LINKS ON THE SLIDE TO
FOLLOW ALONG, AND I'VE ALSO PUBLISHED A
BLOG, AND YOU CAN FOLLOW THAT LAST LINK
AND MESSAGE ME DIRECTLY IF YOU WOULD
LIKE TO WORK ON THE TEAM.
THANK YOU?
>> THANK YOU.
REALLY APPRECIATE THIS INFORMATIVE
PRESENTATION.
HAVE 2 QUESTIONS FOR YOU FROM THE CHAT.
THE FIRST QUESTION SAYS YOU MENTION THE
POTENTIAL RELATIONSHIP BETWEEN WEIGHT
CHANGE OR CHANGE IN BMI AND RISK OF
POSTPARTUM DEPRESSION, HOW DO YOU
RECONCILE THIS WITH THE FACT THAT WEIGHT
GAIN IS NORMAL, UNAVOIDABLE IN
PREGNANCY?
>> RIGHT.
THAT IS UNAVOIDABLE, IT IS A FACT OF
PREGNANCY OF COURSE, THE PURPOSE OF OUR
ANALYSIS IS TO TURN A BLIND EYE TOWARD
WHAT HUMAN BEINGS BELIEVE AND TO LET THE
DATA SPEAK FOR ITSELF, NOW WEIGHT GAIN
IS NATURAL, AND AN EXPECTED PART OF
PREGNANCY BUT THE FACT THAT IT IS ACTING
AS YOUR MENTAL HEALTH AFTERWARD SYSTEM
MAYBE A BIT OF AN OBVIOUS UNDERSTANDING
BUT THERE ARE SEVERAL OTHER
RELATIONSHIPS IN THE ANALYSIS AS WELL,
BETWEEN SOME DATA ABOUT THE HEALTH OF
THE FETAL--FETUS AND OTHER DEVELOPMENTS
AS WELL, LATER ON AND WE THINK MIGHT BE
MORE INDICATIVE.
I JUST THOUGHT THAT THE RELATIONSHIP
BETWEEN WEIGHT GAIN AND DEPRESSION WOULD
BE MORE OBVIOUS AND EASY TO EXPLAIN BUT
THERE ARE SEVERAL OTHERS THAT MIGHT BE
MORE INTERESTING THAN THAT BASIC 1.
>> OKAY.
THANK YOU.
EXCUSE ME, THERE'S 1 OTHER--ACTUALLY 2
MORE.
I THINK WE HAVE TIME FOR THESE.
SECOND IS PRIOR AWARENESS OF MORBIDITY
RISK IS GOOD BUT MAY LEAD TO STIGMA FOR
PREGNANT WOMEN ESPECIALLY IF SEEING
PROVIDERS WHO ARE NOT SUPPORTIVE OR WITH
ACCESS TO LIMITED CARE.
THIS IS A COMMENT IS BASICALLY ASKING IF
HAVE YOU ANY THOUGHTS ABOUT THIS?
>> YEAH THIS, IS A SYSTEMIC PROBLEM THAT
EXISTS WHICH I DO UNDERSTAND THE REALITY
OF WHAT THE PROBLEM WE'RE TRYING TO
ADDRESS.
OUR GOAL IS TO TRY TO IDENTIFY THE WAY
WE CAN TRY TO CATCH THIS EARLY ON, USING
DATA AND MAYBE PROVE THAT WITH SOME OF
THESE STUDIES.
THERE OF COURSE NEEDS TO BE WORK DONE TO
FIX THE SYSTEM TO BE ABLE TO TAKE AND
MAKE USE OF THESE FINDINGS BETTER AND IN
A MORE EFFECTIVE WAY PRIMARILY FOR
UNDERREPRESENTED WOMEN AND THAT'S GOING
TO HAVE TO JUST BE SOMETHING THAT'S
GOING TO HAVE TO HAPPEN.
>> YEAH.
OKAY, AND THE FINAL QUESTION I HAD FOR
YOU, IT SAYS THANK YOU FOR A WONDERFUL
PRESENTATION, AS DELTA IS RECALCULATED
DID YOU LOOK INTO OTHER MODELS WHICH ARE
BETTER WITH TIME SERIES DATA, FIRST
VISIT, SECOND VISIT OR THIRD VISIT LIKE
LSTM-NEUTRAL NETWORKS.
>> NEURAL NETWORKS, YES, WE WERE
CONSIDERING THAT.
UNLIKE SOME OF THE TEAMS THAT CAME INTO
THIS PROJECT WE ACTUALLY FOUND THIS
PROJECT ON FREELANCER SO WE HAD THE
LIMITED 2-3 MONTHS TO WORK WITH IT GIVEN
THE TIME CONSTRAINTS WE DECIDED TO GO
WITH THIS APPROACH BUT NEURAL NETWORKS
WAS A CONSIDERATION FOR US.
THE REASON WE MARKED IT OFF OR DECIDED
NOT TO PURSUE THAT AVENUE IS THROUGH THE
SHEER AMOUNT OF DATA AVAILABLE TO US
WHICH IS LOW.
WE ARE TRYING TO MODEL ON APOs AND A
VERY GOOD ASPECT OF THE DATA AND VERY
BAD MODELS PERSPECTIVES IS THAT THERE IS
A LOW NUMBER OF APOs COMPARED TO OTHER
FREG NANCYS THAT DID NOT DEAL WITH
APOs, RIGHT?
SO FOR THAT REASON, NEURAL NETS PERFORM
AT BEST WHEN GIVEN A HIGH AMOUNT OF
DATA, THERE'S A WELL KNOWN CHART IN THE
MACHINE LEARNING WORLD WHERE OVERTIME,
OVER THE AMOUNT OF DATA AVAILABLE TO US
STANDARD MACHINE LEARNING MODELS
OUTPERFORM UNTIL THE AMOUNT OF DATA
INCREASES TO A SIZE WHERE NEURAL NETS
BECOME MORE EFFECTIVE.
THE AMOUNT OF DATA WE HAVE RIGHT NOW WAS
NOT CONDUCIVE TO A GOOD NEURAL NET.
>> THANK YOU AINESH, WE REALLY
APPRECIATE THAT.
GREAT PRESENTATION.
ALL RIGHT, WE WILL KEEP MOVING AND OUR
NEXT PRESENTER IS FROM THE DELFINI GROUP
AND IT'S THE CHIEF TECHNOLOGY OFFICER
DR. ALI EBRAHIM.
>> THANKS MAURICE FOR THE INTRODUCTION,
HI, I'M ALI AND I'M SUPER EXCITED TO BE
HERE WILL ITING ABOUT THE WORK WE DID AT
DELFINNA FOR THIS PROJECT CHALLENGE OURS
IS TITLED RANDOM FORESTS FOR ACCURATE
PREDICTION OF THE RISK OF HYPER TENSIVE
DISORDERS OF PREGNANCY AT TERM.
I AM SEE HAPPENING A LOT OF SIMILARITIES
WITH RANDOM FORESTS AND HYPER TENSION.
NEXT SLIDE PLEASE.
I THINK A BIG REASON HYPER TENSION WAS A
BIG FOCUS FOR ALL OF US IS THAT HYPER
TENSIVE DISORDERS AND PREGNANCY ARE
EXTREMELY COMMON HERE IN THE UNITED
STATES, THERE ARE STUDIES THAT SHOW THAT
IT COMPLICATES UP TO 10% OF PREGNANCIES.
HYPER TENSIVE DISORDERS CAN INCLUDE
GUESTATIONAL HYPER TENSION, CHRONIC
HYPER TENSION, PREELAMPSIA AND
ECLAMPSIA, AND THEY'RE ALSO ASSOCIATE
WIDE CARDIOVASCULAR DISORDERS AND HYPER
TENSIVE DISORDERS AND RESULT IN A LARGE
RISK FACTOR OF MATERNAL MORBIDITY.
NEXT SLIDE, PLEASE.
SO THIS IS SOMETHING WE REALLY WANT TO
FOCUS ON NOT ONLY BECAUSE IT'S SUCH A
LARGE PROBLEM IN THE UNITED STATES,
RELATIVE TO OTHER COUNTRIES AFFAIRS TEAM
LEADER A SIMILAR SOCIOECONOMIC DLEFUL
LEVEL OF DEVELOPMENT BUT ALSO THAT THIS
RISK IS SIGNIFICANTLY HIGHER FOR
INDIGENOUS AND ALSO BLACK PREGNANT
PEOPLE.
THIS IS A COMMONLY KNOWN PATTERN FOR
MANY RISKS OF MATERNAL MORBIDITY AND
HYPER TENSION ALSO SHOWS THE SAME
PATTERN.
SO THIS IS SOMETHING WE REALLY WANT TO
FOCUS ON.
THE SOCIAL DETERMINANTS OF HEALTH CAN
REALLY NOT BE IGNORED WHEN ADDRESSING
THESE DISPARITIES AND ANALYZING THEM.
AND WE BELIEVE THAT ANY ANALYSIS, OF
CONFOUNDING VARIABLES SUCH AS SYSTEMIC
RACISM THAT ARE PRESENT IN DELIVERY OF
HEALTHCARE AND ALSO OTHER FACTORS THAT
ACCOUNT AFFECT HEALTH OUTCOMES.
NEXT SLIDE, PLEASE.
SO THIS S&P SOMETHING THAT WE'RE REALLY
INTERESTED IN IS PREDICTION OF HYPER
TENSIVE RISK.
SO THE CURRENT STRATEGY THAT MOST
DOCTORS, A LOT OF THE DOCTORS THAT WE
TALK TO IS THEY USE THEIR CLINICAL
JUDGMENT TO ASSESS MATERNAL HISTORY AND
RISK FACTORS AND THEN FROM THAT EACH
DOCTOR WILL BASICALLY DETERMINE THE RISK
OF HIGH RISK PATIENTS, THEY TIKE A LOT
OF FACTORS INTO THEIR ACCOUNT AS THEY
ASSESS THIS.
THERE ARE GUIDELINES THAT ARE PUBLISHED,
AND ALSO THE DOCTORS ARE AWARE OF THE
INDIVIDUAL POPULATION AND WHERE THEY
SERVE, SO THERE ARE SOME STATES WHERE
HYPER TENSIVE RISKS CAN BE UP TO 40% OF
ALL PREGNANCIES MUCH HIGHER THAN 10%
ACROSS THE COUNTRY.
SO THERE IS SOME ROOM THAT DOCTOR SAYS
HAVE TO ADJUST TO HANDLE THE SPECIFIC
HEALTH NEEDS OF THEIR POPULATION.
WE BELIEVE THAT IMPROVED PREDICTION
STRATEGY CAN POTENTIALLY AID DOCTORS AS
THEY MAKE THESE RISK ASSESSMENTS AND
PROVIDE CARE TO THEIR PATIENTS AND IN
THIS WAYS WAY, WE THINK IT WILL ADD TO
THE CARE, SO A WEALTH OF INFORMATION IN
EHR, INCLUDES VITALS, LABS AND
MEDICATIONS THAT CAN DO A MORE
STATISTICALLY SOUND APPROACH TO AID THE
DOCTOR AS THEY THEN FURTHER ASSESS RISK.
WE BELIEVE THAT THIS WILL ALLOW
INCREASED SPECIFICITY AND SENSITIVITY OF
A CLINICIAN USING A MODEL LIKE THIS AND
WE BELIEVE IT CAN ALSO IPT GREATER
IMPROVE CLINICAL MANAGEMENT BY
PERSONALIZING CARE AND BEGINNING
MITIGATIONS EARLY, SO, FOR EXAMPLE, A
PREGNANT PERSON WITH A RISK OF HYPER
TENSIVE DISORDERS CAN BEGIN MONITORING
BLOOD PRESSURE EARLIER SO IF THEY DO FET
REALLY HIGH BLBS THEY GET MUCH MORE
QUICKLY, END UP HAVING THOSE ADDRESSED
BY THEIR PHYSICIAN, AND COME UP WITH A
BETTER STRATEGY AS OPPOSE TO IT COMING
OUT MAYBE MONTHS AFTERWARDS WHEN THEY
HAVE A HIGH BLOOD PRESSURE MEASURED AT
THE DOCTOR'S OFFICE.
ALSO SOMEONE WITH A HIGH RISK OF HYPER
TENSIVE DISORDERS CAN START DOG
SOMETHING LIKE TAKING ASPIRIN WHICH IS
GENERALLY LOW RISK BUT ALSO COULD
MITIGATE SOME STUDIES THEN CAN MITIGATE
THE DISORDERS AND THE DOCTOR CAN AT
THEIR DISCRETION DECIDE IF THIS IS A
NEED OR NOT BASED ON THE PATIENT'S RISK.
SO NEXT SLIDE.
THIS IS A LOT OF OTHER PRESENTERS TALKED
ABOUT HYPER TENSION AND GIIVE A LOT OF
GREAT INFORMATION SO I WANT TO JUMP
RIGHT INTO WHAT WE'VE DONE SO FAR.
SO WE USE THIS NULLIP A ROUS DATA SET,
IT WAS SUCH A WONDERFUL DATA SET,
PLEASURE TO WORK WITH, THE DATA QUALITY
WAS HIGH AND THERE WAS A LARGE NUMBER OF
VARIABLES COLLECTED AND IT MADE IT
REALLY INTERESTING FOR OUR TEAM AND
EVERYBODY WANTED A PIECE OF THIS DATA
ANALYSIS PIE BUT IT WAS ONLY THE LIMITED
NUMBER OF PEOPLE ON OUR TEAM WERE ABLE
TO DO IT.
WHAT WE DID IS WE CREATED MACHINE
LEARNING MODELS TO ASSESS THE RISK OF
THE DISORDERS AND WE BUILT FULL MODELS
AND MINIMIZE MODELS WHICH I WILL GET
INTO IN A LITTLE BIT AND WE MEASURE THE
PERFORMANCE OF ALL OF OUR MODELS AND WE
DEPLOYED OUR MOODLE TO WEB APPLICATION
DEMONSTRATION THAT YOU COULD TYPE DATA
INTO AND GET A SCORE OUT SO NEXT SLIDE,
PLEASE.
>> THE FIRST STEP WAS TO IDENTIFY THE
VARIABLES OF INTEREST, SO OUR INPUT
VARIABLES WERE CLIPICAL KNOWLEDGE FROM 1
OF OUR TEAM MEMBERS, DR. TIMATHY WEN WHO
IS A FETAL AND MATERNAL MEDICINE
SPECIALIST, HE TOOK THE LIST OF 9000
VARIABLE AND IT IS REDUCED IT TO US TO
750 VARIABLES KNOWN DO BE ASSOCIATE WIDE
HYPER TENSION THAT ARE USE INDEED
CLINICAL PRACTICE AND STUDY VS SHOWN
HAVE CORRELATION.
THIS IS INCLUDE LAB RESULTS AND VITALS,
MEDICAL HISTORY, FAMILY HISTORY AND
SOCIAL DETERMINANTS OF HEALTH, 1 INPUT
VARIABLE WHICH WE EXCLUDED FROM TRAINING
BUT KEPT FOR FUTURE ANALYSIS WAS RACE
AND EGHT INISSITY WHICH WE WILL DISCUSS
LATER.
SO FOR OUTCOME VARIABLES WE WANTED TO
SEE IF THE PERSON HAD HYPER TENSIVE
DISORDER FOR PREGNANCY AND THIS WAS THE
PRESENCE OF A DIAGNOOF THETIC COAT,
PREELAMPSIA EITHER MILD OR SEVERE,
ELAMPSIA AND HELLP SYNDROME.
NEXT SLIDE.
ONCE WE KIND OF HAD OUR DATA SET
ASSEMBLES WE CLEANED UP A DATA A BIT TO
PREPARE IT FOR OUR MACHINE LEARNING.
WE USED PYTHON AND COLAB AND PANDAS
STACK FOR OUR ANALYSIS, THERE WERE A FEW
CODING PROBLEMS WHICH WE FIXED AND THEN
1 THING WE DID WAS A 1-HOT ENCODING OF
REPEATED CATEGORICAL FIELDS TO HELP THE
MODELS LEARN BETTER.
A GOOD EXAMPLE IS IF YOU HAVE PEOPLE AND
WHAT THEIR FAVORITE COLORS THEY ARE,
HAVE YOU A FAVORITE COLOR 1 AND 2.
THIS IS NOT THAT CONDUCIVE TO MACHINE
LEARNING ALGORITHM THAT WANTS A CALMER
DATA FORMAT SO WE BROKE THIS DOWN, THIS
IS CALLED 1-HOT ENCODING SO WE WOULD SAY
DO THEY LIKE RED, YELLOW OR BLUE AND
THIS FIRST PATIENT FOR EXAMPLE, LIKES
RED AND BLUE, SO THEY ARE TRUE FOR HAS
RED AND TRUE FOR HAS BLUE, SO THIS
METHOD WAS REALLY VALUABLE FOR A LOT OF
COLUMNS WHICH HAD REPEATED FIELDS LIKE,
YOU HAD ANY FAMILY MEMBER HAD MEDICAL
HISTORY, ET CETERA.
THERE WAS ALSO A MISSING DATA PROBLEM.
WE WANTED TO BE MEASURING RISK AND NOT
WHETHER DATA VARIABLES IS COLLECTED IN
THE DATA SET TO ACTUALLY HAVE THAT BE
THE PREDICTIVE VARIABLE SO ANY VARIABLE
MORE THAN 90% MISSING DATA WE DROPPED
AND THIS LEFT US WITH 518 CO VALID AND
RELIABLEIATES TO PREDICT FROM.
NEXT SLIDE, PLEASE.
THE NEXT THING WE DID AFTER WE HAD A
CLEAN DATA SET WAS TO TRY MACHINE
LEARNING ALGORITHMS SO WE USED A 80/20
TRAINING EVALUATION SPLIT.
WE USED 80% OF THE DATA TO TRAIN AND 20%
WE USED TO EVALUATE.
WE EVALUATED THE AUROC WHICH IS THE AREA
UNDER THE RECEIVER OPERATOR
CHARACTERISTIC CURVE AND WE LOOKED AT
MODEL TRAINING TIMES SO WE USED AUTOML
APPROACHES WHICH IS APPROACHES WHERE YOU
DON'T THINK ABOUT MODEL ARCHITECTURE OR
HOW YOU SEGMENT YOUR DAT AYOU LET THE
MACHINE LEARNING MODEL AT IT AND YOU CAN
SEE WHAT HAPPENS, THESE CAN TAKE MANY,
MANY HOURS TO RUN.
WE ALSO USED SOME HAND TUNE NEURAL
NETWORKS, AND ALSO WE USED SCIKIT-LEARN
RANDOM FORESTS WE USED THE GRID SEARCH
FOR OPTIMAL PARAMETERS TO MAKE THIS
WORK.
WE FOUND THAT SIMILAR TO WHAT OTHER
PANELISTS HAVE DESCRIBED THAT RANDOM
FORESTS WORKED REALLY WELL AND WE FOUND
NO--IT OFTEN OUTPERFORMED THE MORE
COMPLEX MODELS AND THEY TRAIN INDEED A
MATTER OF MINUTES SO WE DEFINITELY USE
THESE MORE SO WE COULD HAVE A QUICKER
TURN AROUND AS WE BUILT THESE MODELS,
NEXT SLIDE, PLEASE.
THE NEXT STEP WAS REDUCING THE NUMBER OF
VARIABLES IN THIS A MINIMIZED MODEL,
THIS MODEL WE HAVE WITH THE CO VALID AND
RELIABLEIATES IS GREAT IF YOU CAN
INTEGRATE WITH AN EHR AND PULL THE DATA
IN WHICH IS STEPS WE'RE BUILDING AT
DELFINNA BUT THAT'S NOT GREAT FOR IF WE
ARE BUILDING SOMETHING FOR AN INDIVIDUAL
TO TRY TO BUILD AND USE.
SO WE USED A FEATURE CALLED RECURSIVE
FEATURE ELIM NATION AND FINDS THE NEXT 1
UNTIL YOU GET DOWN TO 20 VARIABLES, THIS
IS A AGREEDY ALGORITHM, MEANS YOU DON'T
ALL GETD THE BEST RESULT BUT IT'S RUN
EFFICIENTLY AND GREAT FOR
EXPERIMENTATION.
NEXT SLIDE.
THE FINAL STEP FOR WAS US TO REDUCE
RACIAL BIAS IN THE MODELS, SO THIS IS
SOMETHING I REFERRED BACK IN STEP 1, WE
ACTUALLY EXPLICITLY EXCLUDED RACE AND
ETHNICITY FOR THE MODEL BECAUSE WE
DIDN'T WANT THE MODEL TO REENFORCE
EXISTING BIAS IN HEALTH KOIR OUTCOMES
HOWEVER WE FOUND LIKE OTHERS FOUND THAT
THE MODEL SIGNIFICANTLY UNDERPERFORMED
FROM THESE AS WELL AS OTHER GROUPS SO
LAWN MOWER WE DID IS WE USE A TECHNIQUE
CALLED SMOKED WHERE WE WANTED TO BOOST
THE NUMBER OF ROWS IN THE DATA SET FOR
BLACK NONHISPANIC PATIENT IN THE
TRAINING SAMPLE.
>> ONE MINUTE.
>> THANK YOU, THIS FORCES THE OBJECTIVE
FUNCTION THAT THE MODEL IS TRYING TO
TRAIN TO FURTHER REDUCE THE ERROR RATE
FOR THESE PATIENTS RELATIVE TO THE OTHER
GROUPS TO TRY TO BRING THE RESULTS MORE
IN LINE.
AND WE FOUND THIS HELPED US REDUCE THAT
GAP.
NEXT SLIDE, PLEASE.
>> SO, HOW ARE MODELS PERFORMED, WE HAVE
THE FULL MODEL AND THE MINIMIZED MODEL
CONFUSION MATRIXES, THEY PERFORMED
SIMILARLY.
NEXT SLIDE, BETTER WAY TO VIECIALIZE
THIS IS THE AUROC, SO CAN YOU SEE THAT
THE MINIMIZED MODEL IS ALMOST AS GOOD AS
OUR FULL MODEL AND HAS VARIABLES, ON THE
RIGHT YOU SLEEP APNEA AND OBESITYY IT BY
RACE, SO YOU WILL SEE OUR MODEL STILL
UNDERPERFORMS FOR NONHISPANIC BLACK
PATIENTS BUT THE GAP IS MUCH LESS THAN
IT WAS PRIOR TO OUR [INDISCERNIBLE].
SO THE FINAL THING WE DID WAS APPLY IT
TO A WEB APP.
CAN YOU ACCESS THIS AT HYPER
TENSION.DELFINNA.COM.
WE DEPLOY TODAY TO GOOGLE CLOUD FUNCTION
AND DOES A REPROTEIN COMPLEX PROCEDURE
TO THAT FUNCTION AND DISPLAYS THE RESULT
THERE.
I SHOULD NOTE THIS IS ONLY FOR
DEMONSTRATIVE APPROXIMATE UPPERS ONS AND
RESEARCH IT IS NOT MEANT TO BE USED FOR
CLINICAL USE.
AND THAT IS OUR SUBMISSION FROM
DELFINNA, I WANT TO THANK THE TEAM FOR
THE DATA SET AND DASH FOR GIVING US
ACCESS AND ALL THE TEAM MEMBERS FROM
DELFIN WHO WORK WIDE ME ON THIS.
THAT'S IT FOR US.
I AM HAPPY TO TAKE ANY QUESTIONS.
>> THANK YOU VERY MUCH, ALI.
VERY INFORMATIVE PRESENTATION.
THERE IS A COMMENT THAT SAYS, THIS
IS--THAT YOUR PRESENTATION IS VERY
INFORMATIVE AND TO THANK DELFINNA FOR
THEIR COMMITMENT TO HEALTH EQUITY.
>> THANK YOU ANONYMOUS ATTENDEE.
>> YEAH, I'M ALSO GLAD THAT YOU ENJOYED
ANALYZING THE NEW MOMS TO BE DATA SET.
JUST 1 QUESTION, ARE THERE PLANS TO
COMMERCIALIZE YOUR TOOL HERE?
>> THAT'S ACTUALLY SOMETHING WE'RE VERY
INTERESTED IN DOING, WE'RE CURRENTLY
TALKING TO NICHD ABOUT LICENSING DATA
SET FORIE COMMERCIAL APPLICATION, BUT
SEPARATELY WE WANT TO DO THE SAME
TECHNIQUES WE USED AND ACTUALLY WE ARE
WORKING WITH A CLINIC IN SOUTHERN
CALIFORNIA AND WE'RE INTERESTED IN
TRYING TO BUILD MODELS FOR HYPER TENSION
AND HOW THAT--THE DOCTOR IN THAT CLINIC
ACTUALLY USE THEM AND SEE IF IT IMPROVES
CARE, SO THAT'S SOMETHING THAT WE'RE
LASER FOCUSED ON, THANK YOU IF ARE THAT
QUESTION.
>> GREAT.
GREAT DEAL.
THANK YOU.
>> OKAY, OUR NEXT PRESENTER WILL BE
DR. MONICA KEITH WHO IS A DATA SCIENCE
FELLOW AT THE UNIVERSITY OF WASHINGTON
IN SEATTLE.
THERE WE GO.
DR. KEITH?
>> THANK YOU MAURICE, HELLO EVERYONE.
THANK YOU ALL FOR HAVING ME AND FOR
TUNING IN TODAY.
I WORKED WITH MELANIE MARTIN TO DEVELOP
A SOLUTION FOR THIS DATA CHALLENGE.
WE ARE BOTH BIOCULTURAL ANTHROPOLOGISTS
AT THE UNIVERSITY OF WASHINGTON.
AND TODAY I AM SHARING THE STRUCTURAL
EQUATION MODEL THAT WE DEVELOPED, OUR
MODEL IS DESIGNED TO IDENTIFY PATHWAYS
BETWEEN SOCIAL DETERMINANTS OF HEALTH
AND DOWN STREAM MORBIDITYS.
WE FOCUSED ON DISPARITIES AND HYPER
TENSIVE DISORDERS OF PREGNANCY FOR THIS
DATA CHALLENGE AND OUR MODEL IS ALSO
BASED ON THE CONCEPT EVER ALOSTATTIC
LOAD.
NEXT SLIDE.
PLEASE.
SO STARTING WITH THE FRAMEWORK IN SOCIAL
DETERMINANTINGS OF HEALTH, THERE HAS
BEEN MORE AND MORE DISCUSSION RECENTLY
REGARDING SOCIAL DETERMINANTS OF HEALTH.
AND THESE ARE OFTEN BROKEN DOWN AS SUCH
LIKE ECONOMIC AND SOCIAL SUPPORT
METRICS, DIFFERENT HEALTH VARIABLES,
DIFFERENT ACCESS TO AND HELP WITH
HEALTHCARE AND OF COURSE PHYSICAL
ENVIRONMENT AND BUILT ENVIRONMENT AS
WELL.
SO BASED ON THE DATA THAT WERE AVAILABLE
TO US, IN NEW MOM TO BE, WE WERE ABLE TO
MODEL A VARIETIES OF HEALTH BEHAVIORS
AND ALSO SOCIOECONOMIC FACTORS IN OUR
MODEL SOLUTION.
NEXT SLIDE, PLEASE.
SO AS I MENTIONED BEFORE, OUR MODEL IS
ALSO BASED ON THE CONCEPT OF ALOE STATIC
LOAD.
SO ALOE STATIC LOAD IS DEFINED AS THE
CUMULATIVE BURDEN OF PSYCHOLOGICAL AND
PHYSIOLOGICAL STRESS, EXPERIENCED ACROSS
THE LIFE COURSE, SO A MEASURE OF CHRONIC
STRESS, THAT REALLY CONCEPTUALLY LINKS
SOCIAL DETERMINANTS OF HEALTH WITH
CLINICAL BIOMARKERS.
AND CAN YOU SEE ON THE FIGURE TO THE
RIGHT, ON THIS SLIDE, HERE, THAT
BIOMARKERS OF ALOE STATIC LOAD ARE KNOWN
TO ASSOCIATE WITH MANY MATERNAL
MORBIDITYS INCLUDING PREELAMPSIA, WHICH
IS 1 OF THE MORBIDITIES THAT WE FOCUSED
ON IN OUR STRUCTURAL EQUATION MODEL FOR
THIS CHALLENGE, NEXT, SLIDE, PLEASE.
SO WE HYPOTHESIZED HERE THAT THE
CUMULATIVE EFFECTS OF SOCIOLOGICAL
ECOLOGICAL STRESSORS ACT DIRECTLY AND
INDIRECTLY VIA HEALTH BEHAVIORS TO
INCREASE ALOE STATIC LOAD ACROSS
GUESTATION WHICH THEN IN TURN INCREASING
THE RISK OF DEVELOPING MATERNAL
MORBIDITIES AND MORE SPECIFICALLY AN
INCREASED RISK OF HYPER TENSIVE
DISORDERS OF PREGNANCY.
AND THESE INCLUDE DIFFERENT SEVERITYS OF
PREECLAMPSIA AND NEW ONSET HYPER TENSION
DURING OR RESULTING FROM PREGNANCY.
AND AS YOU WILL SEE HERE, THERE ARE ALSO
SIGNIFICANT RACIAL AND ETHNIC
DISPARITIES IN HYPER TENSIVE DISORDERS
OF PREGNANCY WHICH HAS ALSO BEEN
HIGHLIGHTED IN SEVERAL OF THE PREVIOUS
TALKS TODAY AND WE PAUSE IT HERE THAT
THESE DISPARITIES RESULT FROM
SOCIOLOGICAL ECOLOGICAL INEQUITIES AND
RACIALIZED PROCESSES BY WHICH STRESSORS
BECOME ENBODIED AS HEALTH RISKS.
SOPHISTICATEDY WE DECIDES A STRUCTURAL
EQUATION MODEL TO IDENTIFY CAUSAL
PATHWAYS BETWEEN SOCIAL DETERMINANTS,
MEASURES OF ALLOSTATTIC LOAD AND HYPER
TENSIVE DISORD EROZANS OF PREGNANCY AND
WE HYPOTHESIZED THAT THESE FACTORS MAY
VARY IN MAGNITUDE AND SIGNIFICANCE AMONG
RACIAL AND ETHNIC GROUPS SO WE RAN OUR
STRUCTURAL EQUATION MODEL IN A
MULTIGROUP FRAMEWORK TO GET GROUP
SPECIFIC PATH CO-EFFICIENTS.
NEXT SLIDE, PLEASE.
SO I'LL TAKE A MOMENT HERE TO MENTION
THE KEY FEATURES OF STRUCTURAL EQUATION
MODELS, THIS SAY MULTIVALID AND
RELIABLEIATE ANALYSIS THAT MEASURES
RELATIONSHIPS BETWEEN LATENT AND
OBSERVED VARIABLES AND A CAUSAL PATHWAY
FRAMEWORK.
SO THESE MODEL LINEAR RELATIONSHIPS AND
PRODUCE PAST CO EFFICIENTS THAT COUNT
FOR THE VARIANCES AND CO VARIANCES AMONG
THE DATA IN THE MODEL AND STRUCTURAL
EQUATION MODELS HAVE 2 MAIN COMPONENTS.
SO THERE'S THE MEASUREMENTS MODEL OF
RELATIONSHIPS BETWEEN LATENT FACTORS AND
THEIR OBSERVED INDICATOR VARIABLES.
SO IN THE SOCIAL DETERMINANTS FRAMEWORK,
WE WILL HAVE A LATENT FACTOR FOR THE
SOCIAL ENVIRONMENT, OR SOCIOECONOMIC
POSITION.
AND WE HAVE INDICATOR VARIABLES THAT
LOAD ON TO IT, LIKE INCOMMAND
EDUCATIONAL ATTAINMENT BUT THESE ARE
REALLY INCOMPLETE PROXIES OF THE SOCIAL
ENVIRONMENT.
SO THIS LATENT VARIABLE FRAMEWORK IS
PARTICULARLY APPROPRIATE FOR MODELING
SOCIAL DETERMINANTS IN THIS WAY AND THEN
THE STRUCTURAL PART OF THE MODEL HAS ALL
OF THE CONNECTING PATHWAYS, BETWEEN
VARIABLES.
NEXT SLIDE, PLEASE.
SO OUR MODEL HAS 4 LATENT SOCIAL
DETERMINANT FACTORS THAT WE WERE ABLE TO
CHARACTERIZE REALLY ABOUT WITH THIS DATA
SET.
NEXT.
AND THEN WE HAD 3 ALLOSTATTIC LOAD
MEASURES THAT WERE MEASURED AT 4
DIFFERENT TIME POINTS THROUGHOUT
PREGNANCY AS WELL SO THESE 3 MARKERS AS
WELL, THESE ARE DIASTOOL DNAIC BLOOD
PRESSURE AND WEIGHT WERE MEASURED AT 4
DIFFERENT TIMES ACROSS GUESTATION AND WE
MODELED INDIVIDUAL ALLOSTATTIC
TRAJECTORIES AND INTERCEPTED THEIR
TRAJECTORIES AND SLOPES BECAUSE YOU CAN
IMAGINE THAT THE STARTING LEVEL OF BLOOD
PRESSURE AT THE INTERCEPT MAY IMPACT
DOWN STREAM HYPER TENSE OF RISK
DIFFERENTLY THAN THE SCOPE OF BLOOD
PRESSURE CHANGING ACROSS GUESTATION.
NEXT.
AND THEN WE MODELED 5 LEVELS OF HYPER
TENSIVE PREGNANCY OUTCOMES.
NEXT.
AND WE INCLUDED THESE VARIOUS CONTROLS
THAT YOU SEE HERE TO ACCOUNT FOR ASPECTS
OF BASE LINE MATERNAL PHYSIOLOGY, AND
RELEVANT CONFOUNDERS AS WELL.
NEXT; NEXT.
OKAY, THERE WE GO.
YEAH AND THEN WHEN WE FILL IN THE
STRUCTURAL DIAGRAM HERE, WE'RE MODELING
PATHWAYS FROM SOCIAL DETERMINANTS TO
THOSE ALLOSTATTIC LOAD MARKERS AND FROM
THOSE MARKER INTERCEPTS AND SLOPES TO
HYPER TENSIVE OUTCOMES AND WE RAN THIS
MODEL IN R WITH THE LA VON PACKAGE.
NEXT, SLIDE, PLEASE.
SO THIS IS WHAT THE SOCIAL DETERMINANTS
LATENT VARIABLE PORTION OF THE MODEL
LOOKS LIKE.
SO YOU CAN SEE THE OBSERVED INDICATOR
VARIABLES HERE THAT LOAD ON TO THESE
FACTORS AND SINCE WE MODELED THIS IN A
MULTIGROUP MANNER, WE GET GROUP SPECIFIC
COENTIOUS FICIENTS FOR EACH LOADING AND
THERE IS A LOT OF SOCIAL SCIENCE
RESEARCH TO SUPPORT MODELING RACIAL AND
ETHNIC AFFILIATIONS IN IN MANNER, WE
KNOW FOR EXAMPLE, THAT OFTEN TIMES,
BLACK AMERICANS, TAKE ON A LOT MORE DEBT
THAN WHITE AMERICANS TO ACHIEVE THE
EXACT SAME LEVEL OF EDUCATIONAL
ATTAINMENT FOR EXAMPLE.
THERE ARE OFTEN SIGNIFICANT
DIFFERENCINGS OVER WEALTH AND PROPERTY
ENROLLINGSHIP, THINGS THAT ARE OFTEN NOT
DIRECTLY CAPTURED VERY WELL IN OUR DATA
SO WHEN WE'RE RELYING ON METRICS LIKE
INCOME AND EDUCATION LEVEL TO SIGNAL THE
SOCIAL ENVIRONMENT, IT'S IMPORTANT THAT
WE RECOGNIZE THAT THESE METRICS MIGHT
NOT BE INDICATING COMPARABLE
CIRCUMSTANCES, ACROSS GROUPS.
ONE MORE THINK THIS I WILL ADD HERE IS
THAT THESE MODELS ARE VALIDATED BY THEIR
FIT STATISTICS AND I WILL JUST POINT
OUT, YOU CAN SEE IN THE BOX IN THE
BOTTOM RIGHT CORNER THERE THAT WE DO
HAVE STRONG SUPPORT FOR THIS PORTION OF
THE MODEL HERE, FITTING THE DAILY BASIS
THEA REALLY WELL AND WE SEE THE SAME
STRONG SUPPORT AND MODELS FIT FOR THE
FULL STRUCTURAL EQUATION MODEL ON THE
NEXT SLIDE AS WELL.
NEXT SLIDE, PLEASE.
AND HERE ARE OUR FULL MODEL RESULTS, SO
THIS DIAGRAM HERE HAS STATISTICALLY
SIGNIFICANT PAST CO EFFICIENTS ONLILY
SHOWN ON IT AND EXPECTEDLY THESE
CLINICAL MARKERS OF ALLOSTATTIC LOAD ARE
INFORMATIVE OF HYPER TENSIVE RISK.
NOW IF YOU LOOK OVER AT THE MORBIDITY
OUTCOMES ON THE RIGHT HAND SIDE OF THIS
FIGURE HERE, YOU WILL NOTICE THAT BLACK
MOTHER VS WORST HYPER TENSIVE OUTCOMES
AND MORE SEVERE HYPER TENSIVE OUTCOMES
PROPORTIONAL TO THEIR SAMPLE SIZE IN
THESE DATA.
AND THIS MODEL IDENTIFIES SEVERAL
PATHWAYS THAT ARE SPECIFIC TO THIS MOST
ADVERSELY AFFECTED GROUP SO YOU WILL
NOTICE, ALSO THAT NOT ALL OF THE
SIGNIFICANT PATHWAYS ON THIS DIAGRAM ARE
SHARED ACROSS ALL OF THE RACIAL AND
ETHNIC GROUPS.
SO I'LL DRAW YOUR ATTENTION TO THE
LARGEST CO EFFICIENT ON THIS DIAGRAM
WHICH IS THE 1.26 WITH IN BLACK TEXT
THAT'S ON THAT GREEN LINE, GOING FROM
THE SOCIAL ENVIRONMENT TO THE WEIGHT
INTERCEPT AMONG BLACK MOTHERS, SO THAT'S
THE LARGEST PATH CO EFFICIENT IN THIS
ENTIRE MODEL AND THIS ASSOCIATION
BETWEEN THE SOCIAL ENVIRONMENT AND
WEIGHT IS INDEPENDENT OF DIETARY OR
EXERCISE BEHAVIORS WHICH WE HAVE ALSO
ACCOUNTED FOR IN THIS MODEL AND WE
EMPHASIZE THIS HERE AS SOCIAL
SCIENTISTS, SINCE SO MUCH OF THE
DISCUSSION AROUND WEIGHT IN PARTICULAR
IS STILL FOCUSED SO HEAVILY ON
INDIVIDUAL BEHAVIORS AND WHAT ARE
USUALLY DESCRIBED AS HEALTH CHOICES
WHEREAS THESE STANDARDIZED PAST
COENTIOUS FICIENTS HERE ARE HIGHLIGHTING
THAT SOCIAL INIQUITIES ARE REALLY
THE--AT THE ROOT OF THIS PATHWAY.
SO THEN THIS MODEL IDENTIFIED PATHWAYS
AND QUANTIFIED RELATIVE RISK FACTORS FOR
HYPER TENSIVE MORBIDITIES THAT WARRANT
FURTHER RESEARCH, POLICY AND ATTENTION
IN CLINICAL SETTINGS TO ADDRESS HEALTH
DISPARITIES AND WE DO HAVE PLANS TO
EXPAND THIS MODELING FRAMEWORK MOVING
FORWARD TO OTHER HEALTH AND PREGNANCY
OUTCOMES AS WELL.
NEXT SLIDE, PLEASE.
WE WOULD LIKE TO ACKNOWLEDGE THE SUPPORT
AND HELPFUL FEEDBACK ON THIS MODEL FROM
THESE FOLKS HERE.
THANK YOU ALL FOR YOUR TAME AND
ATTENTION TODAY AND I AM HAPPY TO TAKE
QUESTION FIST WE HAVE A FEW MINUTES
HERE.
>> THANK YOU VERY MUCH FOR YOUR VERY
INFORMATIVE PRESENTATION MONICA.
AND HOPE WE DO HAVE SOME TIME.
SO IF THERE ARE BURNING QUESTIONS,
PLEASE TYPE THEM IN THE CHAT AND WE WILL
ASK THOSE QUESTIONS OF OUR PRESENTERS
GOING FORWARD.
AS OF RIGHT NOW, THERE ARE NO QUESTIONS
FOR YOU, BUT THANK YOU VERY MUCH MONICA
FOR YOUR PRESENTATION.
>> THANK YOU.
>> OUR FINAL PRESENTATION IS FROM
DR. NICOLE CARSON FROM THE SCHOOL OF
NURSING ASSISTANT SCHOOL OF NURSING AT
EMORY UNIVERSITY IN ATLANTA.
HER TITLE IS MATERNAL MORBIDITY AND
INTERSECTIONAL SOCIAL DETERMINANTS OF
HEALTH PHENOTYPE.
DR. CARLSON, WELCOME.
>> THANK YOU SO MUCH IT'S A PLEASURE TO
BE HERE WITH YOU ALL, I WILL PRESENT ON
A PROJECT THAT I CONDUCTED WITH
DR. ELISE ERICKSON WHICH WE ARE BOTH
ACADEMIC MIDWIFE SCIENTISTS ABOUT
MATERNAL MORBIDITY AND HOW IT RELATES TO
THE INTERSECTIONAL DETERMINANTS OF
PHENOTYPE IN THE NEW MOMS TO BE DATA
SET.
NEXT SLIDE, PLEASE MPLET I REALLY
APPRECIATE HOW ALL OF THESE PANELISTS
AND PRESENTATIONS HAVE REALLY HELPED 1
ANOTHER TO KIND OF BUILD, SO WE'RE GOING
TO DO A BIT OF BACKGROUND FOR THE WORK,
I WILL INTRODUCE YOU TO OUR THEORETICAL
MODEL AND GET THROUGH KEY METHODS AND
RESULTS AND TAKE AWAYS.
NEXT SLIDE.
SO I WANT TO ACKNOWLEDGE THE FIRST THE
NEW MOMS TO BE STUDY AND DASH FOR
PROVIDING THAT INFORMATION, ALSO BOTH
MYSELF AND DR. ERICSON ARE SUPPORTED BY
THE NIH AND NINR IN OUR WORK.
SO PART 1 IS THE BACKGROUND FOR THE
WORK.
NEXT SLIDE.
SO AS HAS BEEN MENTIONED HERE BY OTHER
PANELISTS, SOCIAL DETERMINANTS OF SHELGT
SOMETHING WE HAVEN'T KNOW SEEN A LOT OF
WORK, KIND OF TRADITIONALLY DONE IN
LOOKING AT MATERNAL MORBIDITY AND SO IN
FACT WHAT WE USUALLY SEE IS MATERNAL
MORBIDITY IS CLINICIAN CLINICALS LIKE
IT'S MORE ABOUT A FUNCTION OF
PREPREGNANCY HEALTH, AGE AND OTHER RISK
FACTORS, RACE AND ETHNICITY IS USED TO
IDENTIFY HIGH RISK PEOPLE WITHOUT
ATTENTION TO THE MECHANISMS OF HOW
MARGINALIZED SOCIAL EXPERIENCE CAN
UNDERPIN THE RELATIONSHIP BETWEEN RACE
AND MORBIDITY AND MORPALTALLITY.
AND SOCIAL FACTORS IF THEY'RE INCLUDED
AT ALL IN STUDIES ARE OFTEN INCLUDED
ONLIA AS CO VALID AND RELIABLEIATES
MAKING IT DIFFICULT TO TRACE THEIR
INTERPLAY.
NEXT SLIDE, PLEASE.
SO YOU'VE ALREADY SEEN INFORMATION ABOUT
THE SOCIAL DETERMINANTS OF HEALTH, THIS
IS AS IT'S DEFINED BY THE INSTITUTE OF
MEDICINE AND SO WHAT DR. ERICSON IS I
WERE INTERESTED IN DOING WAS TO TAKE
ADVANTAGE OF THE NEW MOMS TO BE DATA SET
TO LOOK AT THE SOCIAL DETERMINE NABTS OF
HEALTH AND SEE HOW THEY WOULD HELP
PREDICT MATERNAL MORBIDITY OUTCOMES.
NEXT SLIDE.
PLEASE.
WHAT DREW OUR ATTENTION TO THE NEW MOMS
TO BE DATA SET IS THAT IT IS VERY RICH
IN THE SOCIAL DETERMINANTS OF HEALTH
VARIABLES UNLIKE MANY OTHER CLINICAL
DATA SETS, IT ALSO INCLUDED THE RACE OF
PARTICIPANTS FROM DIFFERENT
PERSPECTIVES.
SO IN THIS AT THAT TIME DATA SET THERE
ARE PEOPLE WHO WOULD IDENTIFY ANCESTRY,
IDENTIFY HOW THEY SELF-IDENTIFIED WITH
RACE AND ETHNICITY AND FINALLY, THEY
IDENTIFIED HOW OTHER PEOPLE IN THE
UNITED STATES PERCEIVED THEIR RACE.
AND IN FACT IT WAS THAT LAST VARIABLE
THAT WE USE INDEED OUR ANALYSIS BECAUSE
THERE'S GOOD EVIDENCE THAT IT IS MORE
HOW PEOPLE ARE TREATED BASED UPON HOW
OTHER PEOPLE PERCEIVE THEIR RACE, THAT
HAS TO DO WITH THINGS LIKE HEALTH
BEHAVIORS FOR EXAMPLE, PEOPLE ARE LESS
LIKELY TO START PRENATAL CARE EARLY IN
PREGNANCY WHEN THEIR SKIN IS DARKER, IT
HAS TO GO BACK TO STRUCTURAL RACISM AND
WE WERE REALLY THRILLED TO HAVE THAT
KIND OF VARIABLE AVAILABLE TO US.
SO WE ALSO INCLUDED PERCEIVED
DISCRIMINATION AS DESCRIBED BY OTHER
PANELISTS AND LABOR PROCESSES AND
OUTCOMES, EMILY 8ITIONS FOR SDOH, NEW
MOM DATA SET FIRST THERE WERE NO
NEIGHBORHOOD LEVEL INFORMATION, SECOND
WE'RE NURSE-MIDWIFE RESEARCHERS SO WE'RE
INTERESTED IN THE CARE ENVIRONMENT AND
THE SPECIFICALLY THE IMPACT OF MIDWIFE
ASHES CYSTED CARE AND THAT WAS NOT
COLLECTED.
NEXT SLIDE.
SO LET'S GO INTO OUR THEORETICAL MODEL,
NEXT SLIDE.
YOU CAN SEE HERE THAT IN BLUE, ARE THE
SOCIAL DETERMINANTS OF HEALTH, VARIABLES
FOR NEW MOMS TO BE.
WE LITRIALY WENT FROM THE IOM MODEL AND
TRIED TO FIND WHERE WE COULD SEE THOSE
SDOH VARIABLES IN THE DATA SET.
AND IDEALLY WHAT WE'RE THINKING HERE IS
THAT THE SOCIAL DETERMINANTS OF HEALTH
REALLY FLOWED THROUGH PREGNANCY
BEHAVIORS, CONDITIONS ASK LABOR AND
BIRTH CHARACTERISTICS TO CREATE TD
CONDITIONS AND MATERNAL MORBIDITY.
WE CREATE THAD FOR THE MORBIDITY OUTCOME
AND YOU CAN SEE IN THE ORANGE BOX WHAT
THE VARIABLES WERE WHICH WERE INCLUDED
IN THAT.
OKAY, NEXT SLIDE, WE WILL TALK ABOUT
METHODS.
WE WERE SPECIFICALLY INTERESTED IN THE
HEALTHY INDIVIDUALS IN THE NEW MOM DATA
SET, THIS IS PEOPLE WHO DIDN'T HAVE
CO-MORBIDITIES COMING INTO THEIR
PREGNANCY, IT WAS OKAY, IF THEY
DEVELOPED CO-MORBIDITIES, IN FACT THAT
WAS SOMETHING THEY WERE INTERESTED IN
BUT WE SELECTED FOR PEOPLE WHO WERE
HEALTHY IN THE OUTSET.
WE WANTED PEOPLE WITH TRIAL LABOR, WITH
NONANATIONAL LIBRARY OF MEDICINE LOWS
FETUS AND THAT MEANS OUR SAMPLE WAS
ALMOST 6000.
IN ACCIDENT WE USED LATENT MIX TOUR
MODELING TO UNCOVER LATENT CLASSES TO
DETERMINE VARIABLES WE USED IN THE DATA
SET AS INDICATORS.
WE CREATED OUR COMPOSITE MEASURE AS I
MENTIONED WHICH FOCUSED ON POSTPARTUM
MORBIDITIES AND WE LOOKED AT BI VALID
AND RELIABLEIATE MODERATION AND
MEDIATION TESTING AND WE DID SEM AND
VISUALIZATIONS TO SEE HOW THESE
INTERACTED AND NEXT SLIDE.
SO FOR OUR RESULTS, NEXT SLIDE, YOU CAN
SLEEP APNEA AND OBESITYY HERE'S OUR
PARTICIPANT SELECTION AND GROUPING.
THIS IS VERY SMALL SO I DON'T EXPECT YOU
TO SEE IT BUT IT GOES THROUGH HOW WE GOT
TO OUR FINAL SAMPLES, AND THE BOTTOM YOU
CAN SEE THAT THE RESULT AFTER LATENT
CLASS MODELING WHERE WE HAD 6 CLASSES
WHICH WERE DESCRIBED AND I WILL TELL YOU
MORE ABOUT THEM IN A MINUTE.
NEXT SLIDE.
SO HERE YOU CAN SEE A VISUALIZATION OF
HOW THESE DIFFERENT SOCIAL DETERMINANTS
OF HEALTH PHENOTYPES DISTRIBUTED ON
THEIR SOCIAL DETERMINANTS OF HEALTH.
SO I WILL DRAW YOUR ATTENTION TO A FEW
IMPORTANT DIFFERENCES FOR EXAMPLE, IN
THE MIDDLE IS CLASS 4, AND YOU NOTICED
THAT THE LIGHT BLUE KIND OF TINT ABOVE
THAT, THAT HAS TO DO WITH THE PERCENT OF
FEDERAL POVERTY LEVEL, CLASS 4 HAD
HIGHEST INCOME.
ALSO DRAW YOUR ATTENTION TO CLASS 4, YOU
WILL SEE THEY HAD THE LOWEST LEVELS OF
PROBLEMS WITH SOCIAL INSECURITY AND
LOWEST LEVELS OF EICATION, AND CLASS 6 I
WILL DRAW YOUR TENSION TO THE FAR RIGHT
OF THE SCREEN, THIS WAS THE GROUP OF
PEOPLE WITH THE LOWEST INCOME, LOWEST
EDUCATIONAL ATTAINMENT, MOST WERE USING
GOVERNMENT HEALTH INSURANCE AND HAD THE
HIGHEST LEVELS OF STRESS OF ALL, AND
THEN I WILL DRAW YOUR ATTENTION TO CLASS
2, THIS WAS THE PEOPLE WHO WERE MOST
RECENTLY IMMIGRATED TO THE U.S.
OVER HALF OF THEM DID NOT SPEAK ENGLISH
WELL, AND THEY DID HAVE 1 OF THE HIGH
LEVELS OF SOCIAL SUPPORT.
NEXT SLIDE, PLEASE.
SO WE ALSO SPLIT THESE GROUPS OUT TO
SHOW PRESENTING RACE AND EGHT INISSITY
BY SELF-REPORT AND CAN YOU SEE THAT
CLASS 4 WHICH WAS THE CLASS OF PEOPLE
THAT WERE THE MOST SOCIALLY SECURE, 94%
WERE WHITE PRESENTING BY CONTRAST CLASS
6 WHICH YOU WILL REMEMBER PEOPLE WHO
WERE THE YOUNGEST, THEY ALSO WERE THE
PEOPLE WITH THE LOWEST EDUCATIONAL
ATAIBMENT AND THE LOWEST INCOME, WAS
SPLIT SOMEWHAT EQUALLY BETWEEN BLACK
PRESENTING HISPANIC PRESENTING AND AND
WHITE PRESENTING.
CLASS 2 WHICH I DRAW YOUR ATTENTION TO
EARLIER WHICH WAS THE CLASS OF PEOPLE
THAT WERE MOST RECENTLY IMMIGRATED TO
THE UNITED STATES, YOU CAN SEE WAS
PREDOMINANTLY WHAT LITTLE OVER HALF OR
HISPANIC PRESENTING.
WE ALSO HAD 24% WHICH WERE WHITE
PRESENTING AND 19% WHICH WERE ASIAN
PRESENTING.
NEXT SLIDE, PLEASE.
OKAY, SO THIS GIVES YOU 1 OF THE FINAL
SLIDES FOR OUR OUR FINDINGS SO ON THE
LEFT, CAN YOU SEE IN THE PIE CHART HOW
THESE CLASSES DISTRIBUTED OVER ENTIRE
SAMPLE OF HEALTHY NULLIPOROUS PEOPLE,
AND YOU CAN SEE AT A GLANCE ALMOST 50%
OF THE WHOLE SAMPLE WAS CLASS 4, THAT
GROUP OF VERY SOCIALLY SECURE, MOSTLY
WHITE PRESENTING PEOPLE, 30% OF OUR
SAMPLE WAS CLASS 6, THAT WAS THE YOUNGER
LESS EDUCATED PEOPLE AND YOU COULD SEE
THE OTHER CLASSES ARE SMALLER
PROPORTIONS.
AND THEN, IT'S A LITTLE BIT HARD TO SEE,
BUT ON THE RIGHT, CAN YOU SEE OUR
PRIMARY MORBIDITY OUTCOMES BY CLASS AND
SO, THE FIRST SET OF BOXES CAN YOU SEE
HERE THAT IS THE COMPOSITE MATERNAL
MORBIDITY OUTCOME.
SO AT A GLANCE, CAN YOU SEE THE DARK
PURPLE IS THE CLASS 4, THE MOST SOCIALLY
PRIVILEGED CLASS AND YOU CAN SEE THAT
THEY HAVE THE LOWEST LEVEL OF COMPOSITE
MATERNAL MORBIDITY OUTCOME, IF YOU LOOK
ACROSS HERE ON THE X-AXIS YOU CAN SEE
FOR SEVERE PREECLAMPSIA, INFECTION,
HEMORRHAGE AND UNPLANNED CAESAREAN THEY
HAD LOWER LEVELS THERE.
BY CONTRAST, CAN YOU SEE THAT THE 2
CLASSES THAT HAD THE HIGHEST LEVEL OF
COMPOSITE MATERNAL MORBIDITY OUTCOMES
CLASS 6 WHICH IS THE ORANGE GROUP AND
CLASS 2 WHICH IS THE RED GROUP.
>> ONE MINUTE.
>> SO KEY TAKE AWAYS, WE DID FIND THE
IRPT SECTIONAL SOCIAL DETERMINANTS OF
HEALTH PREDICTED POSTPARTUM MORBIDITY
INIAL OF OUR ANALYSIS, THESE 2 DISTINCT
GROUPS OF PEOPLE WERE MORE LIKELY TO
HAVE A MORBIDITY EVENT THAT I DESCRIBED
BEFORE.
IN OUR MODERATION ANALYSIS WE FOUND MORE
ADVANCED SERVICEICAL DILATION AT THE
TIME OF HOSPITAL ADMISSION WAS
ASSOCIATED WITH LOWER MATERNAL MORBIDITY
IN CLOTHE CLASS 2 EXPW 6, AND FINALLY IN
OUR FINAL MODEL WE FOUND THAT ONLY
SOCIAL DETERMINANTS OF HEALTH AND
UNPLANNED CAESAREAN BIRTH ARE ASSOCIATE
WIDE THIS PART OF MORBIDITY.
NEXT STEPS WE WILL DO MORE EXPLORATION
OF THE NEW MODELS TO BE DATA SET,
LOOKING AT OTHER VARIABLES WE'RE ALSO
LOOKING TO USE LATENT CLASS ANALYSIS IN
THE FUTURE TO CREATE INTERSKSAL SOCIAL
DETERMINANTS OF HEALTH PHENOTYPES FOR
ANALYSIS EVER PREGNANCY OUTCOMES.
OUR INFORMATION THAT I SHARED HERE HAS
BEEN ACCEPTED FOR PUBLICATION AND SO, IF
YOU'RE INTERESTED IN GETTING IN TOUCH
WITH US AND RESOURCES ARE DOWN AT THE
BOTTOM FOR REFERENCE.
THAT'S IT FOR ME, THANK YOU.
>> THANK YOU DR. CARLSON AND
CONGRATULATIONS TO THE PUBLICATION
THAT'S FOURTH COMING SO DEFINITELY KEEP
US POSTED ON THAT AS WELL.
AND LOOKING AT FUTURE EXPLORATION OF THE
NEW DATA SET, SO.
>> --WE GOT IT CLEANED AT THIS POINT, WE
HAVE TO DO SOMETHING.
>> [LAUGHTER]
>> VERY GOOD, VERY GOOD.
THANK YOU.
WE ARE A LITTLE AHEAD OF SCHEDULE SO IF
ANYONE HAS ANY BURNING QUESTIONS FOR ANY
OF OUR PRESENTERS PLEASE POST YOUR
QUESTIONS AND WE WILL ASK THEM OF ALL OF
OUR PRESENTERS.
I WANT TO THANK EVERYONE AND AT THIS
TIME, I WANT TO MOVE FORWARD AND HAVE
CLOSING REMARKS FROM DR. ALLISON
CERNICH, I WAS JUST LOOKING TO BE
SURE--THERE SHE IS.
HI.
>> GREAT.
MAURICE, I WILL TAKE 1 MORE LITTLE PAUSE
BEFORE I START TALKING I APOLOGIZE MY
SET UP AT WORK IS NOT THE SAME SET UP
THAT I HAVE AT HOME SO I'M ALL OVER THE
PLACE TODAY, TRYING TO FIGURE OUT ALL OF
THE TECHNOLOGY.
FIRST I JUST WANT TO SAY, THANK YOU.
THIS WAS REALLY FUN.
NOT ALL THE TIME DO WE GET TO SAY, THIS
IS REALLY FUN.
FUNNY STORY FOR ALL OF YOU, WHEN WE TOOK
THIS ON IN 2020, I THOUGHT THIS WAS
GOING TO BE THE MOST COMPLICATED THING
THAT I DEALT WITH THAT YEAR.
AND I WAS WRONG BUT I THINK WHAT WAS
REALLY GREAT ABOUT THIS CHALLENGE WAS,
NUMBER 1, I GOT TO WORK WITH AN AMAZING
TEAM AT NICHD, SO I WANT TO FIRST SHOUT
OUT TO ALL OF THE FOLKS AT NICHD WHO
MADE THIS HAPPEN AND TO REALLY DID
ENGAGE IN A LOT OF STRONG DISCUSSION AND
CONFLICT AND WORKING THROUGH HOW TO MAKE
IT ALL WORK AND REALLY FOCUS ON SO TO
MAURICE, TO REGINA, AND THE WHOLE TEAM,
I WANT TO THANK TAYLOR AND BOB, BOTH
FROM THE CHALLENGE OFFICE AT NIH, YOU
WORKED THROUGH THIS AS WELL AS OUR THE
LAWYERS FROM THE COUNSEL AND NICHD
FINANCIAL MANAGEMENT BRANCH, OUR DASH
TEEM AND OUR PARTNERS, AND HONESTLY, THE
INVESTIGATOR WHO IS PROVIDED THIS DATA
TO MATCH THAT MADE THIS CHALLENGE
POSSIBLE.
JUST DIDN'T REALIZE HOW MANY PEOPLE WERE
BEHIND THE SCENES SORT OF MAKE
THANKSGIVING ALL GO.
AND THEY REALLY DID MAKE IT ALL GO SO I
WANT TO THANK THEM AND I WANT TO THANK
OUR COMMUNICATIONS TEAM FOR ALL OF THE
WEB PLATFORM AND ALL THE INFORMATION
THROUGHOUT THE PROCESS AND THANK TO YOU
RECEIVE THAT AND ALL OF THE TEAM AND
COMMUNICATIONS TEAM.
I ALMS WANT TO THANK OW PARTNERS AT
NASA, AND THE COLLABORATIVE CENTER OF
EXCELLENCE, THEY WERE TRULY HELPING US
THE WHOLE WAY THROUGH WITH OUR FIRST
CHALLENGE AND IT WAS COMPLICATED AND
THEY REALLY DID AN OUTSTANDING JOB OF
HOLDING OUR HANDS ESPECIALLY
[INDISCERNIBLE] AND OUR PARTNERS AT
FREELANCER AND [INDISCERNIBLE], I CANNOT
SAY ENOUGH ABOUT HOW PATIENT THEY WERE
WITH US, ESPECIALLY WITH WE WERE
FLAILING.
SO, WHAT'S GREAT IS LOOKING AT THE END
RESULT AND I WILL SAY THE INVESTIGATORS
WHO PRESENTED TODAY, I WANT TO SAY THANK
YOU FOR TAKING THIS CHANCE AND DOING
THIS WORK AND NONAPOPTOTIC TD NOT
GETTING PAID TO DO IT UNTIL THE END.
LOTS OF PEOPLE SORT OF WAIT AROUND BUT
YOU KNOW YOU ALL TOOK THIS DATA AND SAID
HOW CAN WE MAKE A DIFFERENCE IN PUBLIC
HEALTH AND SO, YOU KNOW DID WE GET AS AN
INSTITUTE WHAT WE THOUGHT WE WOULD GET
OUT OF THIS CHALLENGE?
WELL AFTER WATCHING THE PRESENTATIONS
TODAY, YOU KNOW NUMBER 1 DID WE ENGAGE
NEW PEOPLE FROM NEW PLACES WITH THIS
DATA?
I WILL SAY RESOUNDINGLY YES, WE DID.
AND I JUST REALLY WAS EBS SIGHTED TO SEE
SOME OF OUR PUBLIC AND CORPORATE
PARTNERS COMING IN AND LOOKING AND
SAYING WOW, YOU HAVE DATA WE CAN USE TO
SOLVE THE PROBLEMS.
SO THIS WAS A GREAT RESULT.
DID WE SEE NEW MODELS AND NEW
APPROACHES, WE DID.
I WILL SAY THAT SOME OF THESE I THINK
REALLY COULD ADVANCE OUR EXPERIENCE IF
WE LOOK AT NEW DATA MODELS AND NEW
APPROACHES TO THESE DATA THAT HELP US
UNDERSTAND THINGS LIKE,
INTERSECTIONALITY OR CLUSTERED RISK.
AND HELP US MAKE PREDICTIVE MODELS THAT
BETTER IDENTIFY WOMEN WHO ARE GOING TO
BECOME VERY SICK DURING PREGNANCY OR
PEOPLE WHO MAY BECOME VERY SICK DURING
PREGNANCY.
AND I THINK FINALLY, DID WE SEE PEOPLE
APPROACH, QUESTIONS AROUND ETHNIC
DISPARITIES AND THE ANSWER WAS YES,
HEALTH DISPARITIES DID GET GOOD
ATTENTION AND I WAS REALLY EXCITED TO
SEE SOME OF YOU COMBINING THINGS AND ON
THE OTHER HANDING INTERSECTIONALITY AS
IT RELATES TO SOCIAL DETERMINANTS OF
HEALTH THAT UNDERLIE MATERNAL MORBIDITY.
SO, YOU KNOW DID WE MEET THE OBJECTIVES
THAT WE SET OUT WHEN WE SAID WE WOULD
HAVE THIS CHALLENGE?
I THINK WE DID AND WE PROVED THAT YOU
CAN USE DATA USING SECONDARY ANALYSIS
THAT ARE CONSENT TO DATA THAT YOU CAN
SUH USE THOSE TO REALLY GET INNOVATIVE
MODELS.
SO, I CANNOT SAY ENOUGH ABOUT OUR TEAM
AND WHAT THEY DID AND CANNOT SAY ENOUGH
ABOUT THE INVESTIGATOR GROUP THAT
COMPETED IN THIS.
AND I JUST WANT TO SAY THANK YOU FROM
OUR INSTITUTE AND FROM THE NIH
OVERARCHING THERE WAS AN EXCITING
PROJECT.
AND A REAL ACTUAL CHALLENGE TO US AND
WHAT WE DO.
SO, AGAIN, THANKS SO MUCH TO EVERYBODY
AND MAURICE, I WILL TURN IT BACK OVER TO
YOU.
>> THANK YOU VERY MUCH.
I WOULD ALSO LIKE TO ECHO ALLISON'S
SENTIMENT SAYING THANK TO YOU THE ENTIRE
TEAM FOR EVERYTHING YOU'VE DONE.
ALSO A SPECIAL THANK YOU TO OUR WEBINAR
PARTICIPANTS WHO ACTUALLY HELPED PUT
THIS TOGETHER AS WELL AS OUR SIGN
LANGUAGE INTERPRETERS AS WELL.
AND THANKS EVERYONE FOR ATTENDING.
THERE IS 1 QUESTION THAT JUST CAME IN
AND I WANT TO TAKE A LOOK BEFORE I
CONCLUDE OUR MEETING, AND I'M NOT SURE
WHO THIS QUESTION IS DIRECTED TO BUT IF
ANY OF OUR PRESENTERS WOULD LIKE TO
ANSWER THIS QUESTION, THAT WOULD BE
GREAT.
DO HAVE YOU ANY LESSONS LEARNED FROM
DATA MANAGEMENT FROM THE DATA SET WOULD
BE HELPFUL?
>> I'M HAPPY TO SAY SOMETHING?
>> A REPRODUCIBLE FLOW OF DATA
MANAGEMENT IS ABSOLUTELY ESSENTIAL.
THIS IS A LOT OF LITTLE PIECES OF DAILY
BASIS THEA SET THAT HAD TO BE PUT
TOGETHER AND SO BEING ABLE TO DO THAT IN
A REPRODUCIBLE MANNER WAS SUPER HELPFUL
TO BE ABLE TO SEE IT ALL SO THAT WAS OUR
BIG TAKE AWAY.
>> GREAT.
THANK YOU SO MUCH FOR THAT DR. CARLSON.
AGAIN, THANKS EVERYONE FOR
PARTICIPATING, WE ARE ENDING A LITTLE
EARLY BUT I THINK YOU CAN TAKE ADVANTAGE
OF THE NICE WEATHER THAT WE HAVE AT
LEAST IN THE D. C. METROAREA, WE HAVE
NICE WEATHER HERE SO THANK YOU EVERYONE
FOR PARTICIPATING AND WE WILL--THIS WILL
CONCLUDE OUR WINNERS WEBINAR FOR THE
DATA CHALLENGE.
THANK YOU VERY MUCH.