Hands-On Healthcare Data: Taming the Complexity of Real-World Data

Preface
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
1. Introduction to Healthcare Data
	The Enterprise Mindset
	The Complexity of Healthcare Data
	Sources of Healthcare Data
		Electronic Health Records
		Claims Data
		Clinical / Disease Registries
		Clinical Trials Data
	Data Collection and How that Affects Data Scientists
		Retrospective vs. Prospective Studies
	Conclusion
2. Technical Introduction
	Basic Introduction to Docker and Containers
		Installing and Testing Docker
	Conceptual Introduction to Databases
		ACID Compliance
		OLTP Systems
		OLAP Systems
		SQL vs. NoSQL
		SQL Databases
		(Labeled) Property Graph (LPG) Databases
		Hypergraph Databases
		Resource Description Framework (RDF) Databases
		Conclusion
3. Standardized Vocabularies in Healthcare
	Controlled Vocabularies, Terminologies, and Ontologies
	Key Considerations
		Pre-Coordination vs. Post-Coordination
	Case Study Example: EHR Data
	Common Terminologies
		CPT
		ICD-9 and ICD-10
		LOINC
		RxNorm
		SNOMED CT
		Key Takeaways
	Using the Unified Medical Language System (UMLS)
		Some Basic Definitions
		Concept Orientation
		Working with the UMLS
		UMLS and Relational Databases
		Preprocessing the UMLS
		UMLS and Property Graph Databases
		UMLS and Hypergraph Databases
		Review of UMLS
	Conclusion
4. Deep Dive: Electronic Health Records Data
	Publicly Accessible Data
		Medical Information Mart for Intensive Care (MIMIC)
		Synthea
	Data Models
		Goals
		Examples of Data Models
	Case Study: Medications
		The Medication Harmonization Problem
		Technical Deep Dive
		Connecting to the UMLS
	Difficulties Normalizing Structured Medical Data
	Conclusion
5. Deep Dive: Claims Data
	Publicly Accessible Data—SynPUF
	Data Models
		Choosing a Data Model
		Combining Claims and EHR Data
	Case Study: Combining Diagnoses and Medications
		OMOP vs. Graphs
		Considerations when Combining Different Sources of Healthcare Data
	Conclusion
6. Machine Learning and Analytics
	A Primer on Machine Learning
		What is Feature Engineering?
		Graph-based Deep Learning
	Extracting Data as a Table
		To SQL or Not to SQL
		Querying OMOP Data
		From Graphs to Dataframes
		Why Add the Complexity of Graphs?
	Machine Learning and Feature Engineering with Graphs
	Graph Embeddings
		node2vec
		cui2vec
		med2vec
		snomed2vec
		Some Final Thoughts about Embeddings
	Making the Case for Graph-based Analysis
	Conclusion
7. Trends in Healthcare Analytics
	Federated Learning and Federated Analytics
		How Does Federated Learning Work?
		Why Federated Analytics / Learning?
		The Data Harmonization Challenge in a Federated Context
		Graphs and Federated Approaches
	Natural Language Processing
		Concept Extraction
		Beyond Concept Extraction
		Clinical NLP Tools
		Commercial Clinical NLP Solutions
		Key Differences between Clinical NLP and Other Applications of NLP
	Conclusion
8. Graphs, Harmonization, and Some Final Thoughts
	Other Types of Healthcare RWD
	Data Normalization and Harmonization
		Merging Datasets
		Bridging IT and the Business
		It’s a Human, not Technical, Problem
	Graphs Can Be a Part of the Solution
	Graphs Are Not a Silver Bullet
	Conclusion
About the Author