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Andrew Nguyen

Hands-On Healthcare Data

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★★★★★
€84.99
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ai machine learning deep learning emerging technologies ai and health care artificial intelligence deep medicine medical diagnosis health diagnostics
Author_Andrew Nguyen
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COP=United States
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Language_English
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Product details

  • ISBN 9781098112929
  • Dimensions: 178 x 232mm
  • Publication Date: 23 Aug 2022
  • Publisher: O'Reilly Media
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
Delivery/Collection within 10-20 working days

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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 - 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. With this book, you'll learn: The 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
About Andrew Nguyen
Andrew Nguyen has been working at the intersection of healthcare data and machine learning for over a decade. He quickly discovered graph databases and has been using them to harmonize disparate data sources for nearly as long. Andrew holds a PhD in Biological and Medical Informatics from UCSF and a BS in Electrical and Computer Engineering from UCSD. He has worked for a variety of organizations, from academia to startups. He is currently a Principal Medical Informatics Architect at one of the largest biopharma companies in the world, where he is designing scalable solutions to harmonize healthcare real world data sources for machine learning and advanced analytics.