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--once you 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 normalizing 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