A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

Mougiakakou, S; Prountzou, K; Nikita, K (2005). A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients. IEEE Engineering in Medicine and Biology Society conference proceedings, 2006, pp. 298-301. Piscataway, N.J.: IEEE Service Center 10.1109/IEMBS.2005.1616403

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In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition

UniBE Contributor:

Mougiakakou, Stavroula




IEEE Service Center




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Date Deposited:

04 Oct 2013 15:23

Last Modified:

08 Mar 2020 01:50

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https://boris.unibe.ch/id/eprint/37260 (FactScience: 207284)

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