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. 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 dataThe challenges of working with healthcare data, especially when trying to aggregate data from multiple sourcesCurrent options for extracting structured data from clinical textHow to make trade-offs when using tools and frameworks for normalizing structured healthcare dataHow to harmonize healthcare data using terminologies, ontologies, and mappings and crosswalks
