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Andrew Nguyen - Hands-On Healthcare Data: Taming the Complexity of Real-World Data

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Andrew Nguyen Hands-On Healthcare Data: Taming the Complexity of Real-World Data
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Hands-On Healthcare Data: Taming the Complexity of Real-World Data: summary, description and annotation

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Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, youll be able to solve healthcares most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underservedonce 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. Youll discover how graph technologies help you connect disparate data sources so you can solve healthcares most challenging problems using advanced analytics.

With this book, youll 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

Andrew Nguyen: author's other books


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Hands-On Healthcare Data by Andrew Nguyen Copyright 2022 Andrew Nguyen All - photo 1
Hands-On Healthcare Data

by Andrew Nguyen

Copyright 2022 Andrew Nguyen. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com.

  • Acquisitions Editor: Michelle Smith
  • Development Editor: Melissa Potter
  • Production Editor: Christopher Faucher
  • Interior Designer: David Futato
  • Cover Designer: Karen Montgomery
  • Illustrator: Kate Dullea
  • August 2022: First Edition
Revision History for the Early Release
  • 2021-10-26: First Release
  • 2022-03-15: Second Release
  • 2022-06-28: Third Release

See http://oreilly.com/catalog/errata.csp?isbn=9781098112929 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Hands-On Healthcare Data, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

The views expressed in this work are those of the author and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-098-11285-1

Preface
Conventions Used in This Book

The following typographical conventions are used in this book:

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Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://gitlab.com/hands-on-healthcare-data.

If you have a technical question or a problem using the code examples, please send email to .

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Hands-On Healthcare Data by Andrew Nguyen (OReilly). Copyright 2022 Andrew Nguyen, 978-1-098-11292-9.

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .

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We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at https://oreil.ly/healthcare-data.

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Chapter 1. Introduction to Healthcare Data
A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest formthe authors raw and unedited content as they writeso you can take advantage of these technologies long before the official release of these titles.

This will be the 1st chapter of the final book. The GitHub repo for this book is available at https://gitlab.com/andrew-nguyen/hands-on-healthcare-data.

If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the editor at .

Healthcare data is an exciting vertical for data science and there are manyopportunities to have real impact, whether from a clinical or technicalperspective. For patients and clinicians, there is the alluring promise oftruly personalized care where patients gets the right treatment at the righttime, tailored to their genetics, environment, beliefs, and lifestyleeachrequiring the effect integration, harmonization, and analysis of highly complexdata. For data scientists and computer scientists, there are many openproblems for natural language processing, graphs, semantic web, and databases,among many others.

Additionally, there are also frontier problems that arise given the specificcombination of a specific technology and the nuances and complexities ofhealthcare. For example, there is nothing about healthcare data itself nordata science that requires regulatory grade reproducibility. Data scientistsknow how to use version control tools such as Git and IT people know how tocreate database snapshots and use Docker containers. However, regulatorybodies such as the US Food and Drug Administration (FDA) or the EuropeanMedicines Agency (EMA), there are specific requirements to track and storemetadata and other artifacts to prove the results of the analysis, includingreproducibility. Similarly, there is increasing desire and pressure to ensurereproducibility of studies or the sharing of negative results among academics.How we can address these challenges at scale is still unsolved.

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