9780367342906 - Data Driven Approaches for Healthcare: Machine Learning for Identifying High Utilizers (chapman & Hall/crc Big Data Series) by Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay (15 results)
Data Driven Approaches for Healthcare : Machine Learning for Identifying High Utilizers
Yang, Chengliang, Ranka, Sanjay, Delcher, Chris, Shenkman, Elizabeth
Language: English
Published by CRC Press LLC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
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Language: English
Published by Taylor & Francis Ltd 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
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Data-Driven Approaches for Health Care : Machine Learning for Identifying High Utilizers
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Language: English
Published by Taylor & Francis Ltd, London 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Hardcover. Condition: 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 insigh…ts 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.
Data-Driven Approaches for Health Care : Machine Learning for Identifying High Utilizers
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Data-Driven Approaches for Health Care : Machine Learning for Identifying High Utilizers
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
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Data-Driven Approaches for Health Care: Machine Learning for Identifying High Utilizers
Yang, Chengliang/ Delcher, Chris/ Shenkman, Elizabeth/ Ranka, Sanjay
Language: English
Published by Chapman & Hall 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 107 pages. 10.00x7.25x0.50 inches. In Stock.
Data-Driven Approaches for Health Care : Machine Learning for Identifying High Utilizers
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Yang, C: Data Driven Approaches for Healthcare
Chengliang Yang|Chris Delcher|Elizabeth Shenkman|Sanjay Ranka
Language: English
Published by CRC Press 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: moluna, Greven, Germanymoluna
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Gebunden. Condition: New. 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.
Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; Ranka, Sanjay
Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Language: English
Published by Taylor & Francis Ltd 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Hardback. Condition: New. New copy - Usually dispatched within 4 working days.
Language: English
Published by Taylor & Francis Ltd Okt 2019 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. Neuware.
Language: English
Published by Taylor & Francis Ltd, London 2019
Series: Chapman & Hall/CRC Big Data Series, Book 10 of 13. Book 10 of 13 - Chapman & Hall/CRC Big Data Series
- Hardcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Hardcover. Condition: 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 insigh…ts 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.
