Big Data Analytics as Applied to Diabetes Management

Lidong Wang; Cheryl Ann Alexander

doi:doi:10.11648/j.ejcbs.20160205.11

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Phillips, A. (2016). Optimizing the person-centered management of type 2 diabetes. British Journal Of Nursing, 25(10), 535-538 4p.
[2]	Lahiri, S. W. (2016). Management of Type 2 Diabetes in the Setting of Morbid Obesity: How Can Weight Gain Be Prevented or Reversed? Clinical Diabetes, 34(2), 115-120 6p. doi:10.2337/diaclin.34.2.115
[3]	PR, N. (2015, June 23). New Biovista Inc. Publication: EMR Data Confirm Big Data Analytics Prediction That Hypothyroidism is a Risk Factor for New-onset Diabetes Mellitus. PR Newswire US.
[4]	Huntriss, R., & White, H. (2016). Evaluation of a 12-week weight management group for people with type 2 diabetes and pre-diabetes in a multi-ethnic population. Journal Of Diabetes Nursing, 20(2), 65-71 7p.
[5]	Powers M, Bardsley J, Vivian E, et al. Diabetes Self-Management Education and Support in Type 2 Diabetes: A Joint Position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics...Reprinted with permission from Diabetes Care 2015; 38: 1372–1382. Clinical Diabetes [serial online]. Spring2016 2016; 34(2): 70-80 11p. Available from: CINAHL Complete, Ipswich, MA. Accessed June 20, 2016.
[6]	Kleier, J. A., & Welch Dittman, P. (2014). Attitude and Empowerment as Predictors Of Self-Reported Self-Care and A1C Values among African Americans With Diabetes Mellitus. Nephrology Nursing Journal, 41(5), 487-494 8p.
[7]	Praneetsin, C., Pikul, N., Wipada, K., Sirirat, P., Natapong, K., & Turale, S. (2016). Action Research: Development of a Diabetes Care Model in a Community Hospital. Pacific Rim International Journal Of Nursing Research, 20(2), 119-131 13p.
[8]	Williams, J. (2016). Effective team working to improve diabetes care in older people. Journal Of Diabetes Nursing, 20(4), 137-141 5p.
[9]	Tan, C. L., Cheng, K. F., & Wang, W. (2015). Self-care management programme for older adults with diabetes: An integrative literature review. International Journal Of Nursing Practice, 21115-124 10p. doi:10.1111/ijn.12388
[10]	Chasens, E. R., & Luyster, F. S. (2016). Effect of Sleep Disturbances on Quality of Life, Diabetes Self-Care Behavior, and Patient-Reported Outcomes. Diabetes Spectrum, 29(1), 20-23 4p. doi:10.2337/diaspect.29.1.20
[11]	Loucks, E. B., Gilman, S. E., Britton, W. B., Gutman, R., Eaton, C. B., & Buka, S. L. (2016). Associations of Mindfulness with Glucose Regulation and Diabetes. American Journal of Health Behavior, 40(2), 258-267 10p. doi:10.5993/AJHB.40.2.11
[12]	Ellaway, R. H., Pusic, M. V., Galbraith, R. M., & Cameron, T. (2014). Developing the role of big data and analytics in health professional education. Medical Teacher, 36(3), 216-222. doi:10.3109/0142159X.2014.874553
[13]	Bellazzi, R. Dagliati, A. Sacchi, L., & Segagni, D. (2015). New Opportunities for Diabetes Management Big Data Technologies J Diabetes Sci Technol. 2015 Sep; 9(5): 1119–1125. Published online 2015 Apr 24. doi: 10.1177/1932296815583505
[14]	Fox, B. (2011). Using big data for big impact. Leveraging data and analytics provides the foundation for rethinking how to impact patient behavior. Health Management Technology, 32(11), 16.
[15]	May, E. L. (2014). The power of analytics: harnessing big data to improve the quality of care. Healthcare Executive, 29(2), 18.
[16]	Harrison, C. (2012). Deal watch: 'Big data' deal for diabetes clinical trial modelling. Nature Reviews Drug Discovery, 11(11), 822. doi:10.1038/nrd3891
[17]	Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: a cross-sectional, unselected retrospective study. J Biomed Inform. 2016; 54: 162-168. DOI: http://dx.doi.org/10.1016/j.jbi.2015.12.006.
[18]	Saravana kumar N M swari T, S ampath P & Lavanya S. (2015). Predictive Methodology for Diabetic Data Analysis in Big Data., Procedia Computer Science 50 (2015), 203–208.
[19]	Wullianallur R. & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2014. 2-3. DOI: 10.1186/2047-2501-2-3
[20]	GNS, H. (2016). The American Journal of Managed Care Publishes Results Showing Big Data Analytics Can Predict Individualized Risk of Metabolic Syndrome in Patients. Business Wire (English).
[21]	Klonoff, D. C. (2013). The Current Status of mHealth for Diabetes: Will It Be the Next Big Thing? J Diabetes Sci Technol. 2013 May; 7(3): 749–758. Published online 2013 May 1.
[22]	De Silva, D., Burstein, F., Jelinek, H. F., & Stranieri, A. (2015). Addressing the Complexities of Big Data Analytics in Healthcare: The Diabetes Screening Case Australasian Journal of Information Systems 19 • September 2015 DOI: 10.3127/ajis.v19i0.1183
[23]	Hardee, S. G., Osborne, K. C., Njuguna, N., Allis, D., Brewington, D., Patil, S. P., &... Tanenberg, R. J. (2015). Interdisciplinary Diabetes Care: A New Model for Inpatient Diabetes Education. Diabetes Spectrum, 28(4), 276-282 7p. doi:10.2337/diaspect.28.4.276。
[24]	Razavian Narges, Blecker Saul, Schmidt Ann Marie, Smith-McLallen Aaron, Nigam Somesh, & Sontag David. (2016). Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors., Big Data. January 2016, 3(4): 277-287. doi:10.1089/big.2015.0020.
[25]	Marrero, D. G. (2016). Diabetes Care and Research: What Should Be the Next Frontier?...2015 Health Care and Education Presidential Addres. Diabetes Spectrum, 29(1), 54-57 4p. doi:10.2337/diaspect.29.1.54
[26]	Standards of medical care in diabetes--2013. (2013). Diabetes Care, 36 Suppl 1S11-S66. doi:10.2337/dc13-S011
[27]	Cichosz, S.L., Johansen, M. D, Hejlesen, O. (2015). Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications. J Diabetes Sci Technol. Oct 14; 10(1): 27-34. doi: 10.1177/1932296815611680.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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 -

Copy | Download

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download