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Big Data Analytics in Healthcare / Anand J. Kulkarni, Patrick Siarry, Pramod Kumar Singh, Ajith Abraham, Mengjie Zhang, Albert Zomaya, Fazle Baki editors

Contributor(s): Material type: TextSeries: Studies in Big Data ; v. 66Publisher: Cham : Springer, 2020Copyright date: ©2020Edition: 1st edition 2020Description: xi, 187 pages : illustrations (color)Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9783030316716
Subject(s): DDC classification:
  • 23 005.7 KUL
Summary: This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.
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Gifted Books Sai University Library New materials shelf 005.7 KUL (Browse shelf(Opens below)) Available G3019

Gifted by Professor Ajith Abraham

This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.

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