Real-Time Statistical Modeling of Blood Sugar

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 s...

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Bibliographic Details
Published in:Journal of medical systems 2015-10, Vol.39 (10), p.123-123, Article 123
Main Authors: Otoom, Mwaffaq, Alshraideh, Hussam, Almasaeid, Hisham M., López-de-Ipiña, Diego, Bravo, José
Format: Article
Language:English
Subjects:
Algorithms
Blood
Blood Glucose - analysis
Cloud Computing
Diabetes
Diabetes Mellitus - drug therapy
Drug Dosage Calculations
Glucose
Health Informatics
Health Sciences
Historic
Humans
Insulin
Insulin - administration & dosage
Insulin - therapeutic use
Insulin Infusion Systems
Literature reviews
Mathematical models
Medicine
Medicine & Public Health
Mobile Systems
Modelling
Models, Statistical
Neural networks
Patients
Proposals
Real time
Self Care - instrumentation
Smartphone
Statistics for Life Sciences
Sugars
Telemetry - instrumentation
Time Factors
UCAmI & IWAAL 2014
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Summary: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.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-015-0301-8