Zarkogianni, Konstantia; Mougiakakou, Stavroula G; Prountzou, Aikaterini; Vazeou, Andriani; Bartsocas, Christos S; Nikita, Konstantina S (2007). An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods. IEEE Engineering in Medicine and Biology Society conference proceedings, 2007, pp. 5972-5975. Piscataway, N.J.: IEEE Service Center
Full text not available from this repository. (Request a copy)In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.
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: |
18003374 |
Web of Science ID: |
000253467005016 |
URI: |
https://boris.unibe.ch/id/eprint/37254 (FactScience: 207278) |
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