Mougiakakou, Stavroula G; Prountzou, Aikaterini; Iliopoulou, Dimitra; Nikita, Konstantina S; Vazeou, Andriani; Bartsocas, Christos S (2006). Neural network based glucose - insulin metabolism models for children with Type 1 diabetes. IEEE Engineering in Medicine and Biology Society conference proceedings, 2006, pp. 3545-3548. Piscataway, N.J.: IEEE Service Center
Full text not available from this repository. (Request a copy)In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition |
UniBE Contributor: |
Mougiakakou, Stavroula |
ISSN: |
1557-170X |
Publisher: |
IEEE Service Center |
Language: |
English |
Submitter: |
Factscience Import |
Date Deposited: |
04 Oct 2013 15:23 |
Last Modified: |
05 Dec 2022 14:25 |
PubMed ID: |
17947036 |
Web of Science ID: |
000247284706204 |
URI: |
https://boris.unibe.ch/id/eprint/37257 (FactScience: 207281) |
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