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      Book

      Data-Driven Approaches for Health care
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      Book

      Data-Driven Approaches for Health care

      DOI link for Data-Driven Approaches for Health care

      Data-Driven Approaches for Health care book

      Machine Learning for Identifying High Utilizers

      Data-Driven Approaches for Health care

      DOI link for Data-Driven Approaches for Health care

      Data-Driven Approaches for Health care book

      Machine Learning for Identifying High Utilizers
      ByChengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka
      Edition 1st Edition
      First Published 2019
      eBook Published 24 October 2019
      Pub. Location New York
      Imprint Chapman and Hall/CRC
      DOI https://doi.org/10.1201/9780429342769
      Pages 118
      eBook ISBN 9780429342769
      Subjects Computer Science, Economics, Finance, Business & Industry, Engineering & Technology, Health and Social Care, Information Science
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      Yang, C., Delcher, C., Shenkman, E., & Ranka, S. (2019). Data-Driven Approaches for Health care: Machine Learning for Identifying High Utilizers (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429342769

      ABSTRACT

      Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem.

      Key Features:

    • Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes
    • Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers
    • Presents descriptive data driven methods for the high utilizer population
    • Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics
    • TABLE OF CONTENTS

      chapter Chapter 1|6 pages

      Introduction

      chapter Chapter 2|8 pages

      Overview of Health Care Data

      chapter Chapter 3|13 pages

      Machine Learning Modeling from Health Care Data

      chapter Chapter 4|18 pages

      Descriptive Analysis of High Utilizers

      chapter Chapter 5|18 pages

      Residuals Analysis for Identifying High Utilizers

      chapter Chapter 6|21 pages

      Machine Learning Results for High Utilizers

      chapter Chapter 7|2 pages

      Conclusions

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