Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)

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

ISBN 10: 0367342901 / ISBN 13: 9780367342906

Published by Chapman and Hall/CRC, 2019

About this Item

Seller Inventory # 18389062855

Bibliographic Details

• Title: Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2019
• Language: English
• ISBN 10: 0367342901
• ISBN 13: 9780367342906
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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

About the Author

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.