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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.
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Chengliang Yang, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Department of Computer Science, University of Florida.
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Data Driven Approaches for Healthcare : Machine learning for Identifying High Utilizers
Editore:
Taylor & Francis Ltd, 2019
ISBN 10: 0367342901
ISBN 13: 9780367342906
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Yang, C: Data Driven Approaches for Healthcare
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Chengliang Yang, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Depar. Codice articolo 594574941
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Data Driven Approaches for Healthcare: Machine learning Approaches for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
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Chapman and Hall/CRC 2019-10-10, 2019
ISBN 10: 0367342901
ISBN 13: 9780367342906
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Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
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Data-Driven Approaches for Health Care: Machine Learning for Identifying High Utilizers
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Data Driven Approaches for Healthcare (Hardcover)
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Taylor & Francis Ltd, London, 2019
ISBN 10: 0367342901
ISBN 13: 9780367342906
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Hardcover. Condizione: new. Hardcover. 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 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 posed by this problem. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780367342906
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Data Driven Approaches for Healthcare (Hardcover)
Editore:
Taylor & Francis Ltd, London, 2019
ISBN 10: 0367342901
ISBN 13: 9780367342906
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Hardcover. Condizione: new. Hardcover. 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 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 posed by this problem. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9780367342906
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