Data Driven Approaches for Healthcare: Machine learning Approaches for Identifying High Utilizers, 1st Edition (Hardback) book cover

Data Driven Approaches for Healthcare

Machine learning Approaches for Identifying High Utilizers, 1st Edition

By Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka

Chapman and Hall/CRC

112 pages

Purchasing Options:$ = USD
Hardback: 9780367342906
pub: 2019-10-10
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Description

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

    Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utlizers. Residuals Analysis for Identifying High Utilizers.Machine Learning Results for High Utilizers.

    About the Authors

    Chengliang Yang, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Department of Computer Science, University of Florida.

    About the Series

    Chapman & Hall/CRC Big Data Series

    Learn more…

    Subject Categories

    BISAC Subject Codes/Headings:
    BUS070080
    BUSINESS & ECONOMICS / Industries / Service Industries
    COM000000
    COMPUTERS / General
    COM012040
    COMPUTERS / Programming / Games
    COM014000
    COMPUTERS / Computer Science
    COM021000
    COMPUTERS / Database Management / General
    COM021030
    COMPUTERS / Database Management / Data Mining
    COM037000
    COMPUTERS / Machine Theory
    COM062000
    COMPUTERS / Data Modeling & Design
    MED002000
    MEDICAL / Administration
    MED035000
    MEDICAL / Health Care Delivery