Название: Hands-On Healthcare dаta: Taming the Complexity of Real-World Data (Final)Автор: Andrew Nguyen
Издательство: O’Reilly Media, Inc.
Год: 2022
Страниц: 245
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.2 MB
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.
Further chapters dive into sophisticated technical approaches to solving challenging problems in healthcare. These solutions include coverage of topics like graph-based deep learning, commercial clinical NLP solutions, and data harmonization. Andrew covers these topics from a theoretical standpoint, as well as with hands-on examples with working code.
When most of us (myself included) started working with data, we probably wanted to dive straight into Machine Learning and build predictive models. Then, we found ourselves constantly manipulating data, transforming it from one dataframe to another. Nearly every library out there doing something with data expects the input to be in the form of a dataframe, a tabular structure that fits well with data from CSV files and relational databases, but less so with data from document or graph databases.
You'll learn:
Different types of healthcare dаta: 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
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