Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians.
The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data.
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Ashish Khanna is Professor in Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain. His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning.
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.
He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.
Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians.
The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data.
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