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

Foreword
Preface
    Conventions Used in This Book
    Using Code Examples
    O’Reilly Online Learning
    How to Contact Us
    Acknowledgments
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
        Prospective studies
        Retrospective 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 Versus NoSQL
        SQL Databases
        (Labeled) Property Graph Databases
        Hypergraph Databases
        Resource Description Framework Databases
        Conclusion
3. Standardized Vocabularies in Healthcare
    Controlled Vocabularies, Terminologies, and Ontologies
    Key Considerations
        Pre-coordination Versus 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
        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 the UMLS
    Conclusion
4. Deep Dive: Electronic Health Records Data
    Publicly Accessible Data
        Medical Information Mart for Intensive Care
        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 Versus 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 Part of the Solution
    Graphs Are Not a Silver Bullet
    Conclusion
Index
About the Author