This document discusses using a data science platform to enable digital diagnostics in healthcare. It provides an overview of healthcare data sources and Yale/YNHH's data science platform. It then describes the data science journey process using a clinical laboratory use case as an example. The goal is to use big data and machine learning to improve diagnostic reproducibility, throughput, turnaround time, and accuracy for laboratory testing by developing a machine learning algorithm and real-time data processing pipeline.

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EnablingDigitalDiagnostics
WithaDataSciencePlatform
WadeL.Schulz,MD,PhD
DepartmentofLaboratoryMedicine
RichardTorres,MD,MS
DepartmentofLaboratoryMedicine

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Overview
•HealthcareDataandDataSources
•Yale/YNHHDataSciencePlatform
•TheDataScienceJourney:ComputationalHealthcareUseCase
–Question
–Data
–Architecture
–Implementation
•OtherUseCasesinHealthcare

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Healthcare(Big?)Data

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Healthcare(Big?)Data

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TheDreamforBigDatainHealthcare

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TheDataSciencePlatform
K
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b
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iz
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DataScienceJourney

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CreatingDataScienceQuestions
•Definethequestion/hypothesis
•Assessthedatasource,cleanasnecessary
•Iterativelytesttheexperimentandimplementation
DescriptiveDiagnosticPredictivePrescriptive

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ClinicalLaboratoryUseCase
•AdvancedAnalyticsforLaboratoryDiagnostics(Yale-NewHaven)
–~10millionlaboratorytests
–~46millionresultsperyear
–Significantnumberrequirephysicianinterpretation
–Labbasedexample
•Analogoustoanatomic(tissue)pathology
•Applicablebeyond

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GeneralProblemDefinition
•Canweusebigdataandmachinelearningtoimproveourdiagnosticreproducibilityandthroughput
forlaboratorytesting?
•Hypothesis:TheuseofaMLalgorithmandreal-timedataprocessingpipelinewillimproveour
turnaroundtimeandinterpretivereproducibility.Quantitativeresultswillimprovediagnostic
accuracy.