Big Data Analytics as Applied to Diabetes Management
European Journal of Clinical and Biomedical Sciences
Volume 2, Issue 5, October 2016, Pages: 29-38
Received: Sep. 7, 2016; Accepted: Oct. 7, 2016; Published: Oct. 28, 2016
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Authors
Lidong Wang, Department of Engineering Technology, Mississippi Valley State University, Itta Bena, Mississippi, USA
Cheryl Ann Alexander, Technology and Healthcare Solutions, Inc., Itta Bena, Mississippi, USA
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Abstract
Type 2 Diabetes Mellitus (DM) affects many people in the U.S. Among the most affected include women, older adults, and some ethnicities/racial groups. Data from numerous sources are used to detect DM and determine self-care activities. In the following paper we discuss Type 2 Diabetes Mellitus, the role of new technologies in diabetes care, diabetes self-management, and Big Data analytics in diabetes management. It was determined by the data in several articles that by using big data we can predict or diagnose diabetes among undiagnosed patients. A wide variety of data can be managed using big data including the Electronic Medical Record (EMR), pharmacy reports, and laboratory reports, among other data. Also there are new mHealth apps that allow the tracking and reporting of data on a secure, wireless connection, through the cloud, etc. Finally, we need to apply the use of big data in future research to determine the significance of our findings.
Keywords
Diabetes, Big Data Analytics, Electronic Medical Record (EMR), Obesity
To cite this article
Lidong Wang, Cheryl Ann Alexander, Big Data Analytics as Applied to Diabetes Management, European Journal of Clinical and Biomedical Sciences. Vol. 2, No. 5, 2016, pp. 29-38. doi: 10.11648/j.ejcbs.20160205.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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