Title: Health big data analytics: current perspectives, challenges and potential solutions

Authors: Mu-Hsing Kuo; Tony Sahama; Andre W. Kushniruk; Elizabeth M. Borycki; Daniel K. Grunwell

Addresses: School of Health Information Science, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada ' School of Electrical Engineering and Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia ' School of Health Information Science, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada ' School of Health Information Science, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada ' School of Electrical Engineering and Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia

Abstract: Modern health information systems can generate several exabytes of patient data, the so called 'health big data', per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of health big data as well as the challenges and solutions for health big data analytics (BDA) - the process of extracting knowledge from sets of health big data - and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.

Keywords: healthcare technology; big data analytics; BDA; data mining; cloud computing; health information systems; patient data; health big data.

DOI: 10.1504/IJBDI.2014.063835

International Journal of Big Data Intelligence, 2014 Vol.1 No.1/2, pp.114 - 126

Available online: 23 Jul 2014 *

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