LAUSR

Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide. A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR), through the dosing of the insulin bolus to inject before meals. The Artificial Pancreas (AP), combining autonomous insulin delivery and blood glucose monitoring, is a promising solution. However, state-of-the-art APs require several information for bolus delivery, such as the estimated carbohydrate intake over the meals. This is mainly related to the limited knowledge of the determinants of PGR. Although meal carbohydrates are mostly considered as the major factor into, uencing PGR, other food components play a relevant role in PGRs, and thus, should be taken into account. Based on these considerations, a study to determine the effect of nutritional factors (i.e., carbohydrates, proteins, lipids, fibers, and energy intake) in the short and middle term on Blood Glucose Levels (BGLs) prediction was conducted by Machine Learning (ML) methods. A ML model able to predict the BGLs after 15, 30, 45, and 60 minutes from the meal leveraging on insulin doses, blood glucose, and nutritional factors in T1D patients on AP systems was implemented. More specifically, to investigate the impact of the nutritional factors on the model predictions, a Feed-Forward Neural Network, was fed with several dispositions of BGLs, insulin, and nutritional factors. Both public and self-produced data were used to validate the proposal. The results suggest that patient-specific information about nutritional factors can be significant for middle term postprandial BGLs predictions.