acdc: alpha-carving decision chain for risk stratification

ACDC:Alpha-CarvingDecisionChainforRiskStratification

YubinPark,AccordionHealth,Inc.JoyceHo,EmoryUniversity

JoydeepGhosh,TheUniversityofTexasatAustin

1ICMLWHI2016

WhatisDecisionChain(DC)?

• AlsoknownasRuleLists(Wang&Rudin,2015)• Asequenceofrules,appliedtooneafteranother,wheretheratioofpositiveclassincreasesoverthesequenceofrules• ToyExample:AdecisionchainforpredictingthelikelihoodofbeingaLonghornfan,• IfTomlivesinAustin,TXà 25% chanceofbeingaLonghornfan• AndTomlikestowatchfootballgamesà 50% chance• AndTomgoesoutforatailgateoneverySaturdayà 75% chance• AndTomwearsburntoranget-shirtsallthetimeà 95% chance

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Conceptually,somethinglikethis

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IsDCMoreInterpretablethanDT?

• InDecisionChain(DC),• Riskisproportionaltothenumberofrules• Lesstomemorizeforfilteringoutlow-riskpopulation(orsamples)• Moretomemorizeforcapturinghigh-riskpopulation• UsingDC,onecanimplementaneconomicallyefficientbusinessprocessbasedonjobmaturity-level

• WhileinDecisionTree(DT),• Thenumberofrulesisagnostictorisk• Low-riskcanbecapturedwithoneruleaswellashundredsofrules

• Thus,DCmaybehelpfulforsomeapplications

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InHealthcareApplications,

• Classimbalanceproblemsareprevalent• Majorityclassexamplescanbeoftencarvedout(orfilteredout)withasimpleconjunctionofif-elsestatements• Animplementationstrategy• Filteroutmajorityclasswithrulesà ObtainalessimbalanceddatasetàApplyafancymachinelearningalgorithm• Onequestion: HowmanymajorityexamplesshouldIfilterout?• Apossiblesolution: Ifwebuildadecisionchain,thenwecanstreamlinegrid-searchmucheasily

• DCcanbemoreinterpretableaswellasmoreefficient(sometimes)

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QuestionisHow

• Wewilluseagreedyapproach• Notethatdecisiontreeisalsoagreedyalgorithm• Pickasplittingfeaturethatmaximizes{informationgain,purityscore,etc.}• Splitthedatasetintopartsbasedonthevalueofthesplittingfeature• Repeatfromthebeginningforeachdataset

• Wewillgrowadecisionchainasfollows• Pickasplittingfeaturethatcarvesoutthemostamountofmajoritysamples• Splitthedatasetintopartsbasedonthevalueofthesplittingfeature• Repeatfromthebeginningononlyonepartitionthathasmorepositiveclassexamples

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MoreDetailsonHow

• Selectingthebestsplittingfeature• WewilluseAlpha-Divergence• Alpha-DivergenceisthesameasKL-DivergencewhenAlpha=1• Alpha-DivergenceisthesameasHellingerdistancewhenAlpha=0.5• Alpha-DivergencecanbealotofdifferentthingsbasedonthevalueofAlpha

• WewillchangethevalueofAlphaadaptively(withasimplestrategy)toachieveourgoal• Moredetailsareinthepaper

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EffectofDifferentAlphas

• HighAlpha• Purepartitions

• LowAlpha• Balancedpartitions

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α = 1α = 16

α = 48α = 64

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Shock F T

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Experiments:SepticShock

• Alpha-CarvingDecisionChain(ACDC)showscomparableperformancewithotherdecisiontreealgorithms• ATree(a=1):C4.5• ATree(a=2):CART• ATree(a=x):otheralpha-trees

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ATree(a=128)

ACDC

ModelAU

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Experiments:SepticShock

• SinceACDCisadecisionchain,wecanmakethiscoolvisualization• Putdecisionrulesandperformancemetricsinasinglechart• Riskisproportionaltothenumberofrulesapplied

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L1: nbp.systolic<=132

L2: nbp.systolic<=98.4

L3: min.nbp.systolic<=90.4

L4: nbp.diastolic<=46

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0.00 0.25 0.50 0.75 1.00Coverage

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Experiments:CardiacArrest

• Anotherexample:Asystole• Again,comparabletootherdecisiontrees

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ACDC

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Experiments:CardiacArrest

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L1: min.nbp.diastolic<=48.767L2: min.nbp.systolic<=104

L3: min.spo2<=90

L4: spo2<=93.6

L5: min.spo2<=78L6: avg.pp>57.495

L7: hr<=90.9

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0.00 0.25 0.50 0.75 1.00Coverage

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L1: min.nbp.diastolic<=48.767L2: min.nbp.systolic<=104

L3: min.spo2<=90

L4: spo2<=93.6

L5: min.spo2<=78L6: avg.pp>57.495

L7: hr<=90.9

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0.00 0.25 0.50 0.75 1.00FPR

TPR

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Experiments:CardiacArrest

• Youalsocanmakeariskpyramid

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min.diastolic < 48 mmHg

min.systolic < 104 mmHg

min.spo2 < 90 %

spo2 < 93 %

Baseline Risk

1.3 times higher

1.5 times higher

3.7 x

5.8 xAsystole !Risk Stratification !

Decision Chain

Higher risk

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Contacts

• YubinPark• yubin[at]accordionhealth [dot]com

• JoyceHo• joyce [dot]c[dot]ho[at]emory [dot]edu

• JoydeepGhosh• jghosh [at]utexas [dot]edu

14ICMLWHI2016