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BioMed Research International
Volume 2015 (2015), Article ID 370194, 16 pages
http://dx.doi.org/10.1155/2015/370194
Review Article

Big Data Analytics in Healthcare

1Emergency Medicine Department, University of Michigan, Ann Arbor, MI 48109, USA
2University of Michigan Center for Integrative Research in Critical Care (MCIRCC), Ann Arbor, MI 48109, USA
3Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
4Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA

Received 5 January 2015; Revised 26 May 2015; Accepted 16 June 2015

Academic Editor: Xia Li

Copyright © 2015 Ashwin Belle et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.