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      Big Data Analytics in Healthcare

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          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.

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            Big data analytics in healthcare: promise and potential

            Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.
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              High-throughput generation, optimization and analysis of genome-scale metabolic models.

              Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
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                Author and article information

                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi Publishing Corporation
                2314-6133
                2314-6141
                2015
                2 July 2015
                : 2015
                : 370194
                Affiliations
                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
                Author notes
                *S. M. Reza Soroushmehr: ssoroush@ 123456umich.edu

                Academic Editor: Xia Li

                Article
                10.1155/2015/370194
                4503556
                26229957
                cebe2671-2311-4883-947e-37b94af3ebf1
                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.

                History
                : 5 January 2015
                : 26 May 2015
                : 16 June 2015
                Categories
                Review Article

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