Da: Buchpark, Trebbin, Germania
EUR 29,90
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2019
ISBN 10: 0367342901 ISBN 13: 9780367342906
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
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.
Condizione: New.
Condizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 208,62
Quantità: 10 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Majestic Books, Hounslow, Regno Unito
EUR 218,05
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 230,63
Quantità: 10 disponibili
Aggiungi al carrelloCondizione: New.
Da: Revaluation Books, Exeter, Regno Unito
EUR 236,75
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 107 pages. 10.00x7.25x0.50 inches. In Stock.
Da: moluna, Greven, Germania
EUR 200,28
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: 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.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 248,22
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 248,51
Quantità: 1 disponibili
Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days.
Lingua: Inglese
Editore: Taylor & Francis Ltd Okt 2019, 2019
ISBN 10: 0367342901 ISBN 13: 9780367342906
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 245,50
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Neuware.
Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2019
ISBN 10: 0367342901 ISBN 13: 9780367342906
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 307,15
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. 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.