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Sinopsis
This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management.
Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
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Acerca del autor
David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor’s Professor at the University of Nebraska-Lincoln, USA. He has published research in over 200 refereed journal articles and has authored over 40 books. He has served as associate editor of a number of journals and made hundreds of presentations at international and national conferences on research topics. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named Best Enterprise Information Systems Educator by the IFIP in 2006. He is a Fellow of the Decision Sciences Institute.
Dr. Özgür Araz is the Ron and Carol Cope Professor and Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln, USA. His research interests include systems simulation, business analytics, healthcare operations and public health informatics. His research has been supported by the NIH, Veterans Engineering Resource Center (VERC), HDR company, Boys Town of Nebraska, Nebraska Medicine and the University of Nebraska. Before joining the College of Business at UNL, he served at the College of Public Health at the University of Nebraska Medical Center (UNMC). He received his Ph.D. in Industrial Engineering from Arizona State University and was a postdoctoral research fellow at the Center for Computational Biology and Bioinformatics of the University of Texas at Austin. He is an editorial advisory board member of the Transportation Research Part E and also serves as associate editor for Decision Sciences and IISE Transactions on Healthcare Systems Engineering. He is the Public Health Informatics Area Editor for the journal Health Systems. He is also a faculty fellow of the Nebraska Governance and Technology Center and Daugherty Water for Food Global Institute.
De la contraportada
This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management.
Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
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Data Mining and Analytics in Healthcare Management
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Springer, Berlin|Springer Nature Switzerland|Springer, 2024
ISBN 10: 3031281152
ISBN 13: 9783031281150
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today s world. Healthcare management faces a number of challenges, e.g. reducing patient growth th. Nº de ref. del artículo: 1592582804
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Data Mining and Analytics in Healthcare Management
Publicado por
Springer Nature Switzerland Apr 2024, 2024
ISBN 10: 3031281152
ISBN 13: 9783031281150
Nuevo
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 204 pp. Englisch. Nº de ref. del artículo: 9783031281150
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Data Mining and Analytics in Healthcare Management : Applications and Tools
Publicado por
Springer Nature Switzerland, 2024
ISBN 10: 3031281152
ISBN 13: 9783031281150
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today's world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector. Nº de ref. del artículo: 9783031281150
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Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science, 341)
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. 2023rd edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26401133742
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Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science, 341)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Data Mining and Analytics in Healthcare Management
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Springer Nature Switzerland, Springer Nature Switzerland Apr 2024, 2024
ISBN 10: 3031281152
ISBN 13: 9783031281150
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. Neuware -This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today¿s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management.Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch. Nº de ref. del artículo: 9783031281150
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Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science, 341)
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