Hands-On Healthcare Data - Helion

ebook

Autor: Andrew Nguyen
ISBN: 9781098112875
stron: 244, Format: ebook
Data wydania: 2022-08-10
Księgarnia: Helion

Cena książki: 245,65 zł (poprzednio: 285,64 zł)
Oszczędzasz: 14% (-39,99 zł)

Osoby, które kupiły tę książkę, wybierały także »

Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved. But first, you have to learn how to access and make sense of all that data.

This book provides pragmatic and hands-on solutions for working with healthcare data, from data extraction to cleaning and harmonization to feature engineering. Author Andrew Nguyen covers specific ML and deep learning examples with a focus on producing high-quality data. You'll discover how graph technologies help you connect disparate data sources so you can solve healthcare's most challenging problems using advanced analytics.

You'll learn:

Different types of healthcare data: electronic health records, clinical registries and trials, digital health tools, and claims data
The challenges of working with healthcare data, especially when trying to aggregate data from multiple sources
Current options for extracting structured data from clinical text
How to make trade-offs when using tools and frameworks for normalizing structured healthcare data
How to harmonize healthcare data using terminologies, ontologies, and mappings and crosswalks

Osoby które kupowały "Hands-On Healthcare Data", wybierały także:

Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 190,00 zł, (39,90 zł -79%)
Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)

Spis treści

Hands-On Healthcare Data eBook -- spis treści

Foreword
Preface
- Conventions Used in This Book
- Using Code Examples
- OReilly 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
    - Electronic health record versus electronic medical record
    - EHRs and data harmonization
  - 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
    - Cypher
    - Gremlin
    - ArangoDB query language
  - Hypergraph Databases
    - TypeDB and TypeQL
  - Resource Description Framework Databases
    - SPARQL protocol and RDF query language
  - 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
    - Loading UMLS into MySQL
    - Querying UMLS in MySQL
  - Preprocessing the UMLS
  - UMLS and Property Graph Databases
    - Loading UMLS into Neo4j
    - Querying UMLS in Neo4j
  - UMLS and Hypergraph Databases
    - Loading UMLS into TypeDB
    - Querying UMLS in TypeDB
  - Review of the UMLS
- Conclusion
4. Deep Dive: Electronic Health Records Data
- Publicly Accessible Data
  - Medical Information Mart for Intensive Care
    - MIMIC-III schema
      - Patients
      - Admissions
      - ICU stays
      - Transfers
      - Prescriptions
      - Procedure events
      - D_ITEMS
      - D_CPT, D_ICD_DIAGNOSES, D_ICD_PROCEDURES, and D_LABITEMS
    - Limitations
  - Synthea
    - Schema
    - Limitations
- Data Models
  - Goals
  - Examples of Data Models
    - Observational Health Data Sciences and Informatics (OHDSI) OMOP Data Model
      - Working with OMOP
    - Fast Health Interoperability Resources
      - Resources
      - Profiles
      - Implementation guides
    - Other data models
- Case Study: Medications
  - The Medication Harmonization Problem
  - Technical Deep Dive
    - Relational database with SQLite
      - Importing data into SQLite
      - Querying data in SQLite
      - Harmonizing data with SQLite
    - Property graph with Neo4j
      - Importing data into Neo4j
      - Querying data in Neo4j
      - Harmonizing data with Neo4j
    - Hypergraph with TypeDB
      - Import data into TypeDB
      - Querying data in TypeDB
      - Harmonizing data in TypeDB
  - Connecting to the UMLS
- Difficulties Normalizing Structured Medical Data
- Conclusion
5. Deep Dive: Claims Data
- Publicly Accessible DataSynPUF
- Data Models
  - Choosing a Data Model
  - Combining Claims and EHR Data
    - Using OMOP
      - Integrating vocabularies using Athena
      - Mapping tables and columns
    - Using graphs
- Case Study: Combining Diagnoses and Medications
  - OMOP Versus Graphs
  - Considerations When Combining Different Sources of Healthcare Data
    - Structural versus semantic harmonization
    - Using coding systems and crosswalks
- 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
    - Terminology systems
  - Querying OMOP Data
  - From Graphs to Dataframes
    - Neo4j
  - 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
    - Case study example: DayQuil
    - Integrated approaches
  - Beyond Concept Extraction
    - Sepsis prediction
  - Clinical NLP Tools
    - NCBO BioPortal Annotator
    - cTAKES
    - MedSpaCy
  - 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
    - Medical informatics
  - Its a Human, Not Technical, Problem
    - People
    - Process
    - Technology
- Graphs Can Be Part of the Solution
- Graphs Are Not a Silver Bullet
- Conclusion
Index