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Data Mining and Analytics in Healthcare Management - Özgür M. Araz - Bog

Bag om Data Mining and Analytics in Healthcare Management

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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031281129
  • Indbinding:
  • Hardback
  • Sideantal:
  • 204
  • Udgivet:
  • 21. april 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 160x17x241 mm.
  • Vægt:
  • 477 g.
  • 8-11 hverdage.
  • 30. november 2024
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Normalpris

  • BLACK NOVEMBER

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Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Data Mining and Analytics in Healthcare Management

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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