TUM Library catalog › Details for: Data driven approaches for healthcare : machine learning for identifying high utilizers /

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

By: Yang, Chengliang [author.]Contributor(s): Delcher, Chris [author.] | Shenkman, Elizabeth [author.] | Ranka, Sanjay [author.]Material type: Text

TextSeries: Publisher: Boca Raton : CRC Press, Taylor & Francis Group, 2020Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780429342769; 0429342764; 9781000700039; 1000700038; 9781000701258; 1000701255; 9781000700640; 100070064XSubject(s): Medical care -- Utilization -- Mathematical models | Machine learning | BUSINESS & ECONOMICS / Industries / Service Industries | COMPUTERS / General | COMPUTERS / Computer Graphics / Game Programming & DesignDDC classification: 362.1068/3 LOC classification: RA410.6Online resources: Taylor & Francis | OCLC metadata license agreement

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.

Summary: 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

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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

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