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

By: and and and

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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
Copyright:
2019

Book Details

Book Quality:
Publisher Quality
Book Size:
108 Pages
ISBN-13:
9781000701258
Related ISBNs:
9781032088686, 9780367342906, 9780429342769
Publisher:
CRC Press
Date of Addition:
Copyrighted By:
Taylor & Francis Group, LLC
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Business and Finance, Medicine
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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