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Data mining and analytics in healthcare managementapplications and tools /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data mining and analytics in healthcare managementby David L. Olson, Ozgur M. Araz.
Reminder of title:	applications and tools /
Author:	Olson, David L.
other author:	Araz, Ozgur M.
Published:	Cham :Springer Nature Switzerland :2023.
Description:	x, 191 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Health services administrationData processing.
Online resource:	https://doi.org/10.1007/978-3-031-28113-6
ISBN:	9783031281136$q(electronic bk.)

Data mining and analytics in healthcare managementapplications and tools /
Olson, David L.

Data mining and analytics in healthcare managementapplications and tools /[electronic resource] :by David L. Olson, Ozgur M. Araz. - Cham :Springer Nature Switzerland :2023. - x, 191 p. :ill. (some col.), digital ;24 cm. - International series in operations research & management science,v. 3412214-7934 ;. - International series in operations research & management science ;v. 140..

Chapter 1: Urgency in Healthcare Data Analytics -- Chapter 2: Analytics and Knowledge Management in Healthcare -- Chapter 3: Visualization -- Chapter 4: Association Rules -- Chapter 5: Cluster Analysis -- Chapter 6: Time Series Forecasting -- Chapter 7: Classification Models -- Chapter 8: Applications of Predictive Data Mining in Healthcare -- Chapter 9: Decision Analysis and Applications in Healthcare -- Chapter 10: Analysis of Four Medical Datasets -- Chapter 11: Multiple Criteria Decision Models in Healthcare- Chapter 12: Naïve Bayes Models in Healthcare -- Chapter 13: Summation.

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.

ISBN: 9783031281136$q(electronic bk.)

Standard No.: 10.1007/978-3-031-28113-6doiSubjects--Topical Terms:

250131
Health services administration
--Data processing.

LC Class. No.: RA971.6 / .O47 2023

Dewey Class. No.: 362.102856312

Data mining and analytics in healthcare managementapplications and tools /
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505 0 $a Chapter 1: Urgency in Healthcare Data Analytics -- Chapter 2: Analytics and Knowledge Management in Healthcare -- Chapter 3: Visualization -- Chapter 4: Association Rules -- Chapter 5: Cluster Analysis -- Chapter 6: Time Series Forecasting -- Chapter 7: Classification Models -- Chapter 8: Applications of Predictive Data Mining in Healthcare -- Chapter 9: Decision Analysis and Applications in Healthcare -- Chapter 10: Analysis of Four Medical Datasets -- Chapter 11: Multiple Criteria Decision Models in Healthcare- Chapter 12: Naïve Bayes Models in Healthcare -- Chapter 13: Summation.
520 $a 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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650 2 4 $a Health Informatics. $3 274212
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