Skip to main content

Advertisement

SpringerLink
Log in

Real-Time Statistical Modeling of Blood Sugar

  • Mobile Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient’s historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Andreassen, S., Benn, J.J., Hovorka, R., Olesen, K.G., Carson, E.R., A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. Comput. Methods Prog. Biomed. 41(3):153–165, 1994.

    Article  CAS  Google Scholar 

  2. Akematsu, Y., and Tsuji, M., Economic effect of eHealth: Focusing on the reduction of days spent for treatment. In: 11th International Conference on e-Health Networking, Applications and Services, Healthcom, 2009.

  3. Alasaarela, E., Nemana, R., DeMello, S., Drivers and challenges of wireless solutions in future healthcare. In: International Conference on eHealth, Telemedicine, and Social Medicine.

  4. Andrianasy, F., and Milgram, M., Applying neural networks to adjust insulin-pump doses. In: Proceedings of the 1997 IEEE Workshop Neural Networks for Signal Processing VII.

  5. Alshraideh H., and Runger G., Process Monitoring Using Hidden Markov Models. Qual. Reliab. Eng. Int. 30:13791387, 2014.

    Google Scholar 

  6. Alshraideh, H., and Khatatbeh, E., A Gaussian Process Control Chart for Monitoring Autocorrelated Process Data. J. Qual. Technol. 46(4):317–322, 2014.

    Google Scholar 

  7. Campos-Cornejo, F., and Campos-DelgadoD, U., Self-Tuning Insulin Dosing Algorithm for Glucose Regulation in Type 1 Diabetic Patients. Health Care Exchanges. PAHCE. Pan American (2009)

  8. Jordanova M.M., eHealth: from space medicine to civil healthcare. In: Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005

  9. King, A.B., Clark, D., Wolfe, G.S., How much do I give? Dose estimation formulas for once-nightly insulin glargine and premeal insulin lispro in type 1 diabetes mellitus. Endocr. Pract. 18(3):382–386, 2012.

    Article  PubMed  Google Scholar 

  10. Klonoff, D.C., Buse, J.B., Nielsen, L.L., Guan, X., Bowlus, C.L., Holcombe, J.H., Maggs, D.G., Exenatide effects on diabetes, obesity, cardiovascular risk factors and hepatic biomarkers in patients with type 2 diabetes treated for at least 3 years. Curr. Med. Res. Opin. 24(1):275–286, 2007.

    Article  Google Scholar 

  11. Otoom, M., Alshraideh, H., Almasaeid, H., López-de-Ipiña, Diego, Bravo, J., A Real-Time Insulin Injection System. In: Proceedings of the Ambient Assisted Living and Active Aging - 5th International Work-Conference, IWAAL, pp. 120–127, 2013.

  12. Rizza, R. A., Mandarino, L.J., Gerich, J.E., Dose-response characteristics for effects of insulin on production and utilization of glucose in man. American Journal of Physiology-Endocrinology And Metabolism 240(6):E630–E639, 1981.

    CAS  Google Scholar 

  13. Ross, S.M., Introduction to Probability Models. Tenth Edition: Elsevier AP, 2010.

    Google Scholar 

  14. Shimauchi, T., Kugai, N., Nagata, N., Takatani, O., Microcomputer-aided insulin dose determination in intensified conventional insulin therapy. IEEE Transactions on Biomedical Engineering. 2013.

  15. Stein, O.S., Eirik, A., Ragnar, M.J., Fred, G., Statistical Modeling of Aggregated Lifestyle and Blood Glucose Data in Type 1 Diabetes Patients. In: Second International Conference on eHealth, Telemedicine, and Social Medicine, 2010.

  16. Taha, H.A., Operations Research: An Introduction. Ninth Edition. New Jersey: Prentice Hall, 2010.

    Google Scholar 

  17. Pickup, J.C., Insulin-pump therapy for type 1 diabetes mellitus. N. Engl. J. Med. 366(17):1616–1624, 2012.

    Article  CAS  PubMed  Google Scholar 

  18. Vasilyeva, E., Pechenizkiy, M., Puuronen, S., Towards the framework of adaptive user interfaces for eHealth. In: 18th IEEE Symposium on Computer-Based Medical Systems, 2005.

  19. Wallace, T.M., and Matthews, D.R., The assessment of insulin resistance in man. Diabet. Med. 19(7): 527–534, 2002.

    Article  CAS  PubMed  Google Scholar 

  20. Wang, N., and Kang, G.A., Monitoring system for type 2 diabetes mellitus. In: IEEE 14th International Conference on e-Health Networking, Applications and Service (Healthcom), pp. 62–67, 2012.

  21. World Health Organization, http://who.int/mediacentre/factsheets/fs312/en/index.html. Accessed on July 2013.

Download references

Author information

Authors and Affiliations

  1. Yarmouk University, Irbid, Jordan

    Mwaffaq Otoom & Hisham M. Almasaeid

  2. Jordan University of Science and Technology, Irbid, Jordan

    Hussam Alshraideh

  3. University of Deusto, Bilbao, Spain

    Diego López-de-Ipiña

  4. Castilla-La Mancha University, Ciudad Real, Spain

    José Bravo

Authors
  1. Mwaffaq Otoom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hussam Alshraideh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hisham M. Almasaeid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diego López-de-Ipiña
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Bravo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mwaffaq Otoom.

Additional information

This article is part of the Topical Collection on UCAmI & IWAAL 2014

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Otoom, M., Alshraideh, H., Almasaeid, H.M. et al. Real-Time Statistical Modeling of Blood Sugar. J Med Syst 39, 123 (2015). https://doi.org/10.1007/s10916-015-0301-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0301-8

Keywords

Search

Navigation