Book description
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
Publisher resources
Table of contents
- Foreword
- Preface
- 1. Introduction to Healthcare Data
- 2. Technical Introduction
- 3. Standardized Vocabularies in Healthcare
- 4. Deep Dive: Electronic Health Records Data
- 5. Deep Dive: Claims Data
- 6. Machine Learning and Analytics
- 7. Trends in Healthcare Analytics
- 8. Graphs, Harmonization, and Some Final Thoughts
- Index
- About the Author
Product information
- Title: Hands-On Healthcare Data
- Author(s):
- Release date: August 2022
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098112929
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