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

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Chengliang Yang Chris Delcher (University of Kentucky, KY, USA)

Data Driven Approaches for Healthcare

Machine learning for Identifying High Utilizers

Chengliang Yang, Chris Delcher (University of Kentucky, KY, USA), Elizabeth Shenkman (University of Florida, FL. USA) , Sanjay Ranka (University of Florida, Gainesville, USA)

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English

Chapman & Hall/CRC
30 June 2021

Health & safety aspects of computing; Databases; Machine learning

Series: Chapman & Hall/CRC Big Data Series

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

By: Chengliang Yang, Chris Delcher (University of Kentucky, KY, USA), Elizabeth Shenkman (University of Florida, FL. USA), Sanjay Ranka (University of Florida, Gainesville, USA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 220g
ISBN: 9781032088686
ISBN 10: 1032088680
Series: Chapman & Hall/CRC Big Data Series
Pages: 120
Publication Date: 30 June 2021
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Paperback
Publisher's Status: Active

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.