Librería: Majestic Books, Hounslow, Reino Unido
EUR 66,56
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 75,22
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 74,37
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 72,66
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Librería: Chiron Media, Wallingford, Reino Unido
EUR 70,74
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Añadir al carritoPaperback. Condición: New.
Librería: Better World Books Ltd, Dunfermline, Reino Unido
EUR 97,60
Cantidad disponible: 1 disponibles
Añadir al carritoCondición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Librería: Buchpark, Trebbin, Alemania
EUR 72,49
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Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 196,08
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2019
ISBN 10: 0367342901 ISBN 13: 9780367342906
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 198,48
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: 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.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 208,43
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 209,87
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 223,20
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 242,60
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 238,11
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Añadir al carritoHardcover. Condición: Brand New. 107 pages. 10.00x7.25x0.50 inches. In Stock.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 233,84
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Librería: moluna, Greven, Alemania
EUR 204,59
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Añadir al carritoGebunden. Condición: 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.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 247,86
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 249,94
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Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days.
Idioma: Inglés
Publicado por Taylor & Francis Ltd Okt 2019, 2019
ISBN 10: 0367342901 ISBN 13: 9780367342906
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 250,80
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Añadir al carritoBuch. Condición: Neu. Neuware.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2019
ISBN 10: 0367342901 ISBN 13: 9780367342906
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 302,39
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: 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.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
EUR 78,53
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 75,37
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Añadir al carritoPAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: moluna, Greven, Alemania
EUR 55,75
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Añadir al carritoCondición: 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.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 91,94
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.