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Biomed Res Int. 2015;2015:370194. doi: 10.1155/2015/370194. Epub 2015 Jul 2.

Big Data Analytics in Healthcare.

Author information

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

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.

PMID:
26229957
PMCID:
PMC4503556
DOI:
10.1155/2015/370194
[Indexed for MEDLINE]
Free PMC Article

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