A dual mode adaptive basal-bolus advisor based on reinforcement learning

Sun, Qingnan; Jankovic, Marko; Budzinski, João; Moore, Brett; Diem, Peter; Stettler, Christoph; Mougiakakou, Stavroula (2019). A dual mode adaptive basal-bolus advisor based on reinforcement learning. IEEE journal of biomedical and health informatics, 23(6), pp. 2633-2641. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2018.2887067

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Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients’ glucose level on the previous day. The ABBA is based on reinforcement learning (RL), a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimisation and achieve glucose control - independently of the type of glucose monitoring technology.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Sun, Qingnan; Jankovic, Marko; Diem, Peter; Stettler, Christoph and Mougiakakou, Stavroula


600 Technology > 610 Medicine & health
600 Technology > 620 Engineering




Institute of Electrical and Electronics Engineers




Stavroula Mougiakakou

Date Deposited:

04 Feb 2019 08:27

Last Modified:

14 Nov 2019 01:30

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Diabetes, insulin treatment personalisation, reinforcement learning, artificial intelligence, adaptive system



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