Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes

Daskalaki, Eleni; Diem, Peter; Mougiakakou, Stavroula (2016). Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes. PLoS ONE, 11(7), e0158722. Public Library of Science 10.1371/journal.pone.0158722

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Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.

Item Type:

Journal Article (Original Article)

Division/Institute:

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

UniBE Contributor:

Daskalaki, Eleni; Diem, Peter and Mougiakakou, Stavroula

Subjects:

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

ISSN:

1932-6203

Publisher:

Public Library of Science

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

10 Aug 2016 15:08

Last Modified:

14 Aug 2016 02:09

Publisher DOI:

10.1371/journal.pone.0158722

PubMed ID:

27441367

BORIS DOI:

10.7892/boris.85769

URI:

https://boris.unibe.ch/id/eprint/85769

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