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

By Elena Daskalaki, Peter Diem, Stavroula G. Mougiakakou. 2016

67 citations recorded by the source corpus.

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.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics, Surgery

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Citations in the corpus

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning (2019, methodology): “The research presented here is a continuation of our previous studies [14], [18] and targets the entire insulin pump population of adults with diabetes Type 1, independently of the technology used for glucose monitoring.”
Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units (2019, background): “…learning (RL) [1] has achieved tremendous achievements in solving complex sequential decision making problems in various health care domains, including treatment in HIV [2], cancer [3], diabetics [4], anaemia [5], schizophrenia [6], epilepsy [7], anesthesia [8], and sepsis [9], just to name a few.”
Artificial Intelligence for Diabetes Management and Decision Support: Literature Review (2018, methodology): “Another AP study based on RL was proposed by Daskalaki et al, who proposed using an Actor-Critic algorithm to optimize insulin infusion for personalized glucose regulation, and evaluated the system using virtual patients [27].”