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

Mougiakakou, SG; Prountzou, K; Nikita, KS

dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Prountzou, K	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:43:05Z
dc.date.available	2014-03-01T02:43:05Z
dc.date.issued	2005	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31218
dc.subject	Compartmental models	en
dc.subject	Diabetes mellilus	en
dc.subject	Neural networks	en
dc.subject	RTRL algorithm	en
dc.subject	Simulation	en
dc.subject.other	Glucose	en
dc.subject.other	Insulin	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Patient treatment	en
dc.subject.other	Real time systems	en
dc.subject.other	Compartmental models	en
dc.subject.other	Diabetes mellilus	en
dc.subject.other	RTRL algorithm	en
dc.subject.other	Metabolism	en
dc.title	A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2005.1616403	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2005.1616403	en
heal.identifier.secondary	1616403	en
heal.publicationDate	2005	en
heal.abstract	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. © 2005 IEEE.	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.2005.1616403	en
dc.identifier.volume	7 VOLS	en
dc.identifier.spage	298	en
dc.identifier.epage	301	en