Improving diabetes conventional therapy via machine learning modeling

Glucose is a major source of energy for the human body and it is essential that blood glucose levels are maintained within a safe range. Type 1 Diabetes (T1D) is a metabolic disorder characterized by the deficiency of insulin, a hormone which is secreted by the pancreas and is responsible for blood glucose regulation. Thus, T1D patients need exogenous insulin injections to keep the blood glucose level within a safe range. However, the post-prandial (PP) glucose regulation remains a challenging issue for diabetes treatment. In order to improve PP glucose concentrations, a data-driven modeling approach to adjust the meal-related insulin dose is proposed. Specifically, an individualized regression model able to correct the meal bolus computed with the conventional therapy is developed in order to handle the inter-patient variability characterising T1D patients that may affect PP glucose regulation. Moreover, the proposed approach exploits specific models for different day periods on the basis of the intra-day variability of insulin sensitivity. The individualized therapy is validated both on nominal and perturbed scenarios by using the UVA/PADOVA simulator, which is accepted by the FDA as a substitute for pre-clinical animal trials, and the results of a case study are reported.