Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy

Sun, Qingnan; Jankovic, Marko; Mougiakakou, Stavroula (27 July 2019). Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy (In Press). In: 41st International Engineering in Medicine and Biology Conference (IEEE EMBC2019). Berlin. 23-27/07/2019.

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The existing adaptive basal-bolus advisor (ABBA) was further developed to benefit patients under insulin therapy with multiple daily injections (MDI). Three different in silico experiments were conducted with the DMMS.R simulator to validate the approach of combined use of self-monitoring of blood glucose (SMBG) and insulin injection devices, e.g. insulin pen, as are used by the majority of type 1 diabetes patients under insulin therapy. The proposed approach outperforms the conventional method, as it increases the time spent within the target range and simultaneously reduces the risks of hyperglycaemic and hypoglycaemic events.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
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 and Mougiakakou, Stavroula

Subjects:

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

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

30 Aug 2019 14:14

Last Modified:

30 Aug 2019 14:14

BORIS DOI:

10.7892/boris.132002

URI:

https://boris.unibe.ch/id/eprint/132002

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