BulkLearningonEHRData

Po-Hsiang Chiu, George Hripcsak Department of Biomedical Informatics

Columbia University

InaNutshell…

Bulk Learning is a batch-phenotyping method/framework that uses multiple diseases collectively (i.e. bulk learning set) as a substrate for model learning and evaluation in which model stacking is used to construct abstract feature representation of low sample complexity in order to reduce training requirements.

Phenotyping

•  Defini<on

source:h?p://www.evolu<on.berkeley.edu/

•  Diseasesandsubtypes•  Concept-drivendiseasecohorts

–  100infec<ousdiseasesasthedomainofstudy(i.e.bulklearningset)–  Phenotypicmodelsassociatedwithlabtests,medicinalprescrip<ons

•  Dimensionalityreduc<on

BulkLearningBasicsI

•  ABatch-phenotypingmethod/framework•  Addressestwocentralissuesinpredic<veanaly<calapproachtocomputa<onalphenotyping–  Featureengineering

•  Medicalontologyforfeaturedecomposi<on•  E.g.MED(h?p://med.dmi.columbia.edu)

– Dataannota<on•  Ensemblelearning(e.g.stackedgeneraliza<on[Wolpert1992])

•  Featureabstrac<onfordimensionalityreduc<on

BulkLearningBasicsII

•  Usesdiagnos<ccodes(e.g.ICD-9)assurrogatelabelstoestablish“approximatepredic<vemodels.”

•  Whysurrogatelabels(e.g.ICD-9)?–  FeaturesextractedfromEHRcanbelarge– Morecompactrepresenta<onofthetrainingdata–  “Free”supervisedsignalsthataresufficientlyclosebutcanbeobtainedwithoutextrawork

•  Objec<ve:Buildsta<s<calmodelsinabstractfeaturespace–  Createasmallannota<onset(i.e.goldstandard)thatservesaproxydatasetfordownstreammodelevalua<ons

BulkLearningBasicsIII•  Whyinspec<ngmul<ple(infec<ous)diseases?

–  Usingmul&plediseasesassubstrateandiden<fyingtheircommonelements–  Examplestackingarchitecture(understackedgeneraliza<onmethod)

Level 1

Level 0

Antibiotic Measure

Urinary Chemistry Measure

Intravenous Chemistry Measure

Microbiology Measure

Level 2

Attributes: Level-0 Probabilities and IndicatorsTarget: Diagnostic Codes (Silver Standard)

Other Phenotypic Measures (e.g. Antiviral)

Attributes: Level-1 Probabilities and ICD-9Target: True Labels (Gold Standard)

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Level 0 Level 1

Microbiology

An<bio<c

Bloodtest

Urinetest

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microbiology

antibioticblood test

urine test

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Four Example Base Models

127.4Enterobiasis

047.8(Other)viralmeningi<s

009.1Gastroenteri<s...

053.9Herpezzoster

117.9Mycoses

MovingForward•  Summary

–  Bulklearningisaframeworkwithatleastthefollowingsystemchoices•  Thebulklearningset(oftargetcondi<ons)=>basemodels•  Classifica<onalgorithms(guideline:probabilis<cclassifiers+well-calibrated)•  Stackingarchitecture(mul<ple<ers=>levelsofabstrac<ons)•  Strategyforcombiningindividual(local)diseasemodelstoaglobalmodel

–  Advantage:Canuseasmallannotatedsampleformodelconstruc<onandevalua<onwithintheabstractfeaturespace(e.g.level-1data)

•  83clinicalcaseswerelabeledinthisstudy(tobediscussedmorecomprehensively)–  Challenge:Themodelinvolvingtheinterac<onbetweenabstractfeaturesand

ICD-9donotgeneralizewellintotheregionofthedatawheretheICD-9codingwasincorrect

•  Mul<pletypesofsurrogatelabels⌃

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Semi-supervisedlearningAc&velearning

Complexdecisionboundary?

Othersurrogatelabels

•  Ongoingandfuturework

T H A N K

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Reference[1]D.H.Wolpert,Stackedgeneraliza<on,NeuralNetworks.5(1992)241–259.[2]K.M.Ting,I.H.Wi?en,Issuesinstackedgeneraliza<on,J.Ar<f.Intell.Res.10(1999)271–289.[3]J.JinChen,C.ChengWang,R.RunshengWang,UsingStackedGeneraliza<ontoCombineSVMsinMagnitudeandShapeFeatureSpacesforClassifica<onofHyperspectralData,IEEETrans.Geosci.RemoteSens.47(2009)2193-2205.[4]DavidBaorto,JamesCimino,etal.Available:h?p://med.dmi.columbia.edu.Accessdate:Oct20,2016.

MedicalOntology

•  SnapshotofMedicalEn<<esDic<onary(h?p://med.dmi.columbia.edu)

ExampleFeatures

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microbiology

antibioticblood test

urine test

2. Compute Base Models

Level-1 Global Unit

Individual Level-1 Local Units

Level-1 abstractfeatures

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Four Example Base Models

3. Compute Meta Models (via Ensemble Learning)1. Define Feature Groups Using Medical Ontology

1a. Gather EHR data according to medical concepts

1b. Use Medical Entities Dictionary to delineate feature scopes

1c. Apply feature selection within each

concept group

3a. Per-disease ensembles:compute local level-1 models

3b. Cross-disease ensemble: compute a global

level-1 model

Global level-1 features