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020 a| 9780367342906q| (hardback ;q| alk. paper);

020 a| 0367342901q| (hardback ;q| alk. paper);

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100 1 a| Yang, Chengliangc| (Of University of Florida)?| UNAUTHORIZED;

245 10 a| Data-driven approaches for health careh| [electronic resource] :b| machine learning for identifying high utilizers /c| Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.;

260 a| Boca Raton :b| CRC Press,c| [2020];

300 a| ix, 107 pages :b| illustrations ;c| 26 cm;

490 1 a| Chapman & Hall/CRC big data series;

500 a| "A Chapman & Hall book.";

500 a| Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utilizers. Residuals Analysis for Identifying High Utilizers. Machine Learning Results for High Utilizers.;

504 a| Includes bibliographical references and index.;

505 0 a| 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.;

506 a| Available only to authorized users.;

520 a| 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.--c| Source other than the Library of Congress.;

538 a| Mode of access: World Wide Web;

650 0 a| Medical carex| Utilizationx| Mathematical models.=| ^A9933;

650 0 a| Machine learning.=| ^A145211;

650 12 a| Medical Overusex| prevention & control=| ^A1380272;

650 22 a| Medical Overusex| statistics & numerical data=| ^A1380270;

650 22 a| Models, Theoretical=| ^A918806;

650 22 a| Machine Learning=| ^A1345552;

650 7 a| Machine learning.2| fast0| (OCoLC)fst01004795;

651 2 a| United States=| ^A965939;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Delcher, Chris.?| UNAUTHORIZED;

700 1 a| Shenkman, Elizabeth.?| UNAUTHORIZED;

700 1 a| Ranka, Sanjay.=| ^A272046;

830 0 a| Chapman & Hall/CRC big data series.=| ^A1316290;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9780429342769;

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Data-driven approaches for health care machine learning for identifying high utilizers / Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.

Author/creator	Yang, Chengliang
Other author	Delcher, Chris.
Other author	Shenkman, Elizabeth.
Other author	Ranka, Sanjay.
Format	Electronic
Publication Info	Boca Raton : CRC Press, [2020]
Description	ix, 107 pages : illustrations ; 26 cm
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Medical care--Utilization. --Mathematical models. Machine learning. Medical Overuse--prevention & control. Medical Overuse--statistics & numerical data. Models, Theoretical Machine Learning United States

Series	Chapman & Hall/CRC big data series Chapman & Hall/CRC big data series. ^A1316290
Contents	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.
Abstract	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.-- Source other than the Library of Congress.
General note	"A Chapman & Hall book."
General note	Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utilizers. Residuals Analysis for Identifying High Utilizers. Machine Learning Results for High Utilizers.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Issued in other form	Electronic version: Yang, Chengliang. Data driven approaches for healthcare. Boca Raton : CRC Press, Taylor & Francis Group, 2020 9780429342769
Genre/form	Electronic books.
LCCN	2019941841
ISBN	9780367342906 (hardback ; alk. paper)
ISBN	0367342901 (hardback ; alk. paper)

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Data-driven approaches for health care machine learning for identifying high utilizers / Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.

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