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 eeering. 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 challeg 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 teologies, ontologies, and mappings and crosswalks



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