An Actor-Critic based controller for glucose regulation in type 1 diabetes

Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula (2012). An Actor-Critic based controller for glucose regulation in type 1 diabetes. Computer methods and programs in biomedicine, 109(2), pp. 116-125. Amsterdam: Elsevier 10.1016/j.cmpb.2010.02.010

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A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.

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:	Diem, Peter, Mougiakakou, Stavroula
Subjects:	600 Technology > 610 Medicine & health
ISSN:	0169-2607
Publisher:	Elsevier
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 14:26
Last Modified:	05 Dec 2022 14:07
Publisher DOI:	10.1016/j.cmpb.2010.02.010
PubMed ID:	22502983
URI:	https://boris.unibe.ch/id/eprint/9382 (FactScience: 215112)