title: Real-Time Statistical Modeling of Blood Sugar.
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.
authors: Yarmouk University, Irbid, Jordan, motoom@gmail.com.
authors: Mwaffaq
authors: M
authors: Otoom
authors: Hussam
authors: H
authors: Alshraideh
authors: Hisham M
authors: HM
authors: Almasaeid
authors: Diego
authors: D
authors: López-de-Ipiña
authors: José
authors: J
authors: Bravo
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D000067917:Cloud Computing; D003920:Diabetes Mellitus / Q000188:drug therapy*; D054796:Drug Dosage Calculations; D006801:Humans; D007328:Insulin / Q000008:administration & dosage* / Q000627:therapeutic use; D007332:Insulin Infusion Systems; D015233:Models, Statistical; D012648:Self Care / Q000295:instrumentation*; D000068997:Smartphone; D013686:Telemetry / Q000295:instrumentation; D013997:Time Factors
chemical_list: D001786:Blood Glucose; D007328:Insulin