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Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers.

Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers.

・ISBN 978-1-032-08868-6 paper GB£ 51.99

￥16,470.- (税込) ※（※）価格はご注文時の参考価格となります。
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 9780429342769

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著者・編者Yang, Chengliang / Delcher, Chris / Shenkman, Elizabeth / Ranka, Sanjay,
シリーズ (Chapman & Hall/CRC Big Data Series)
出版社 (Chapman & Hall/CRC, UK)
出版年月2021
ページ数120 pp.
言語ENG
ニュース番号＜A01-67055＞

解説

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