03710cam a2200577Ii 4500 9780429342769 FlBoTFG 20260210180712.0 m o d cr cnu|||unuuu 191003s2020 flu ob 001 0 eng d OCoLC-P eng rda pn OCoLC-P 9780429342769 (electronic bk.) 0429342764 (electronic bk.) 9781000700039 (electronic bk. : PDF) 1000700038 (electronic bk. : PDF) 9780367342906 9781000701258 (electronic bk. : EPUB) 1000701255 (electronic bk. : EPUB) 9781000700640 (electronic bk. : Mobipocket) 100070064X (electronic bk. : Mobipocket) (OCoLC)1121596821 (OCoLC-P)1121596821 RA410.6 BUS 070080 bisacsh COM 000000 bisacsh COM 012040 bisacsh UY bicssc 362.1068/3 23 Yang, Chengliang, author. Data driven approaches for healthcare : machine learning for identifying high utilizers / Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka. Boca Raton : CRC Press, Taylor & Francis Group, 2020. 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier 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 Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utlizers. Residuals Analysis for Identifying High Utilizers.Machine Learning Results for High Utilizers. OCLC-licensed vendor bibliographic record. Medical care Utilization Mathematical models. Machine learning. 1623 BUSINESS & ECONOMICS / Industries / Service Industries bisacsh COMPUTERS / General bisacsh COMPUTERS / Computer Graphics / Game Programming & Design bisacsh Delcher, Chris, author. Shenkman, Elizabeth, author. Ranka, Sanjay, author. Taylor & Francis https://www.taylorfrancis.com/books/9780429342769 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 88943 88942