Data Driven Approaches for Healthcare

Data Driven Approaches for Healthcare

AngličtinaEbook
Yang, Chengliang
CRC Press
EAN: 9781000700039
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Podrobné informace

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 codesProvides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizersPresents descriptive data driven methods for the high utilizer populationIdentifies a best-fitting linear and tree-based regression model to account for patients' acute and chronic condition loads and demographic characteristics
EAN 9781000700039
ISBN 1000700038
Typ produktu Ebook
Vydavatel CRC Press
Datum vydání 1. října 2019
Stránky 118
Jazyk English
Země Uruguay
Autoři Delcher, Chris; Ranka, Sanjay; Shenkman, Elizabeth; Yang, Chengliang
Série Chapman & Hall/CRC Big Data Series
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