An insulin infusion advisory system based on autotuning nonlinear model-predictive control

Zarkogianni, Konstantia; Vazeou, Andriani; Mougiakakou, Stavroula; Prountzou, Aikaterini; Nikita, Konstantina S (2011). An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE transactions on biomedical engineering, 58(9), pp. 2467-77. New York, N.Y.: Institute of Electrical and Electronics Engineers IEEE 10.1109/TBME.2011.2157823

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This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Item Type:	Journal Article (Original Article)
Division/Institute:	10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition 04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
UniBE Contributor:	Mougiakakou, Stavroula
ISSN:	0018-9294
Publisher:	Institute of Electrical and Electronics Engineers IEEE
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 14:14
Last Modified:	05 Dec 2022 14:03
Publisher DOI:	10.1109/TBME.2011.2157823
PubMed ID:	21622071
URI:	https://boris.unibe.ch/id/eprint/3474 (FactScience: 207273)