Big Data Analytics as Applied to Diabetes Management

Lidong Wang; Cheryl Ann Alexander

doi:doi:10.11648/j.ejcbs.20160205.11

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Big Data Analytics as Applied to Diabetes Management

Lidong Wang, Cheryl Ann Alexander

Published in European Journal of Clinical and Biomedical Sciences (Volume 2, Issue 5)

Received: 7 September 2016 Accepted: 7 October 2016 Published: 28 October 2016

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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.

Published in	European Journal of Clinical and Biomedical Sciences (Volume 2, Issue 5)
DOI	10.11648/j.ejcbs.20160205.11
Page(s)	29-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Diabetes, Big Data Analytics, Electronic Medical Record (EMR), Obesity

References

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APA Style

Lidong Wang, Cheryl Ann Alexander. (2016). Big Data Analytics as Applied to Diabetes Management. European Journal of Clinical and Biomedical Sciences, 2(5), 29-38. https://doi.org/10.11648/j.ejcbs.20160205.11

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ACS Style

Lidong Wang; Cheryl Ann Alexander. Big Data Analytics as Applied to Diabetes Management. Eur. J. Clin. Biomed. Sci. 2016, 2(5), 29-38. doi: 10.11648/j.ejcbs.20160205.11

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AMA Style

Lidong Wang, Cheryl Ann Alexander. Big Data Analytics as Applied to Diabetes Management. Eur J Clin Biomed Sci. 2016;2(5):29-38. doi: 10.11648/j.ejcbs.20160205.11

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@article{10.11648/j.ejcbs.20160205.11,
  author = {Lidong Wang and Cheryl Ann Alexander},
  title = {Big Data Analytics as Applied to Diabetes Management},
  journal = {European Journal of Clinical and Biomedical Sciences},
  volume = {2},
  number = {5},
  pages = {29-38},
  doi = {10.11648/j.ejcbs.20160205.11},
  url = {https://doi.org/10.11648/j.ejcbs.20160205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ejcbs.20160205.11},
  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.},
 year = {2016}
}

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TY - JOUR
T1 - Big Data Analytics as Applied to Diabetes Management
AU - Lidong Wang
AU - Cheryl Ann Alexander
Y1 - 2016/10/28
PY - 2016
N1 - https://doi.org/10.11648/j.ejcbs.20160205.11
DO - 10.11648/j.ejcbs.20160205.11
T2 - European Journal of Clinical and Biomedical Sciences
JF - European Journal of Clinical and Biomedical Sciences
JO - European Journal of Clinical and Biomedical Sciences
SP - 29
EP - 38
PB - Science Publishing Group
SN - 2575-5005
UR - https://doi.org/10.11648/j.ejcbs.20160205.11
AB - 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.
VL - 2
IS - 5
ER -

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Author Information

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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APA Style

Lidong Wang, Cheryl Ann Alexander. (2016). Big Data Analytics as Applied to Diabetes Management. European Journal of Clinical and Biomedical Sciences, 2(5), 29-38. https://doi.org/10.11648/j.ejcbs.20160205.11

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ACS Style

Lidong Wang; Cheryl Ann Alexander. Big Data Analytics as Applied to Diabetes Management. Eur. J. Clin. Biomed. Sci. 2016, 2(5), 29-38. doi: 10.11648/j.ejcbs.20160205.11

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AMA Style

Lidong Wang, Cheryl Ann Alexander. Big Data Analytics as Applied to Diabetes Management. Eur J Clin Biomed Sci. 2016;2(5):29-38. doi: 10.11648/j.ejcbs.20160205.11

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@article{10.11648/j.ejcbs.20160205.11,
  author = {Lidong Wang and Cheryl Ann Alexander},
  title = {Big Data Analytics as Applied to Diabetes Management},
  journal = {European Journal of Clinical and Biomedical Sciences},
  volume = {2},
  number = {5},
  pages = {29-38},
  doi = {10.11648/j.ejcbs.20160205.11},
  url = {https://doi.org/10.11648/j.ejcbs.20160205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ejcbs.20160205.11},
  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.},
 year = {2016}
}

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