Safe and Personalized Meal Bolus Calculator for Type-1 Diabetes Using Bayesian Optimization.

Abstract

Despite several developments in artificial pancreas technology, postprandial glycemic regulation remains to be a major challenge for type 1 diabetes management. Typically, the large spike in blood glucose concentration induced by meals require an appropriate dose of bolus insulin. Although matching bolus insulin to carbohydrate intake has been shown to improve glycemic regulation, current state-of-the-art meal bolus calculators depend on patient-specific parameters and/or historical clinical data, which may not be easily available. In this paper, we propose a model-free safe and personalized bolus calculator algorithm that is based on safe contextual Bayesian optimization. The proposed algorithm neither requires any patient-specific parameters, nor historical clinical data. Furthermore, the proposed algorithm focuses on patient safety, and ensures satisfaction of the safety-critical hypoglycemia constraint with high probability. In silico experiments conducted on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator, as well as on a cohort of 50 virtual patients based on the Hovorka T1D model in open-loop mode, demonstrate that our algorithm is able to quickly learn the optimum bolus insulin dose for the announced meals using only the patient's CGM data while ensuring patient safety.