book

Hands-On Healthcare Data

by Andrew Nguyen

August 2022

Beginner to intermediate

242 pages

7h 22m

English

O'Reilly Media, Inc.

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Conventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
The Enterprise MindsetThe Complexity of Healthcare DataSources of Healthcare DataElectronic Health RecordsClaims DataClinical/Disease RegistriesClinical Trials DataData Collection and How That Affects Data ScientistsProspective studiesRetrospective studiesConclusion
Basic Introduction to Docker and ContainersInstalling and Testing DockerConceptual Introduction to DatabasesACID ComplianceOLTP SystemsOLAP SystemsSQL Versus NoSQLSQL Databases(Labeled) Property Graph DatabasesHypergraph DatabasesResource Description Framework DatabasesConclusion
Controlled Vocabularies, Terminologies, and OntologiesKey ConsiderationsPre-coordination Versus Post-coordinationCase Study Example: EHR DataCommon TerminologiesCPTICD-9 and ICD-10LOINCRxNormSNOMED CTKey TakeawaysUsing the Unified Medical Language SystemSome Basic DefinitionsConcept OrientationWorking with the UMLSUMLS and Relational DatabasesPreprocessing the UMLSUMLS and Property Graph DatabasesUMLS and Hypergraph DatabasesReview of the UMLSConclusion
Publicly Accessible DataMedical Information Mart for Intensive CareSyntheaData ModelsGoalsExamples of Data ModelsCase Study: MedicationsThe Medication Harmonization ProblemTechnical Deep DiveConnecting to the UMLSDifficulties Normalizing Structured Medical DataConclusion
Publicly Accessible Data—SynPUFData ModelsChoosing a Data ModelCombining Claims and EHR DataCase Study: Combining Diagnoses and MedicationsOMOP Versus GraphsConsiderations When Combining Different Sources of Healthcare DataConclusion
A Primer on Machine LearningWhat Is Feature Engineering?Graph-Based Deep LearningExtracting Data as a TableTo SQL or Not to SQLQuerying OMOP DataFrom Graphs to DataframesWhy Add the Complexity of Graphs?Machine Learning and Feature Engineering with GraphsGraph Embeddingsnode2veccui2vecmed2vecsnomed2vecSome Final Thoughts About EmbeddingsMaking the Case for Graph-Based AnalysisConclusion
Federated Learning and Federated AnalyticsHow Does Federated Learning Work?Why Federated Analytics/Learning?The Data Harmonization Challenge in a Federated ContextGraphs and Federated ApproachesNatural Language ProcessingConcept ExtractionBeyond Concept ExtractionClinical NLP ToolsCommercial Clinical NLP SolutionsKey Differences Between Clinical NLP and Other Applications of NLPConclusion
Other Types of Healthcare RWDData Normalization and HarmonizationMerging DatasetsBridging IT and the BusinessIt’s a Human, Not Technical, ProblemGraphs Can Be Part of the SolutionGraphs Are Not a Silver BulletConclusion

Content preview from Hands-On Healthcare Data

Preface

A few years ago, I was at the Google Faculty Institute, where I met Noah Gift during one of the lunch breaks. We got to talking about academia and education, and many of the challenges and opportunities we saw when it came to empowering people to become experts in data. Whether this was data engineering, data science, or even the more basic aspects of programming, we both saw the potential for fundamentally changing how knowledge is disseminated.

It was shortly after this conversation that Noah floated the idea of writing a book. While I had considered this previously, it was a fleeting thought and not something I had seriously considered. I filed the conversation away in the back of my mind and figured it could be the focus of my sabbatical (I was still in academia at the time).

A year later, everything was flipped upside down by COVID-19 and the world’s response. Despite having just received tenure and promotion, I decided to leave academia and return to industry—rolling up my sleeves and getting back into the thick of it.

I was a few months into my first project (building a clinicogenomic database that pulled data from a handful of hospitals) when I started to see opportunities to help educate our teams on how we could improve our approach to dealing with the complexities of electronic health record (EHR) data.

By then, we were deep into the pandemic and all riding the roller-coaster of repeated loosening and tightening of the many COVID restrictions. Every day, I saw ...