Meal Detection and Carbohydrates Counting for In-Silico Type 1 Diabetic Patients Based on Supervised Learning

Several control strategies aimed at developing an artificial pancreas (AP) for patients with type 1 diabetes mellitus exhibit significant limitations. Specifically, the glucose ingested by the patient cannot be directly measured; instead, only its effect (e.g., an increase in blood glucose levels) can be observed. As a result, aggressively compensating for high blood glucose levels following postprandial episodes with high insulin rates does not always ensure blood glucose levels remain within a healthy range and may instead expose patients to potential hypoglycemic conditions. Some control approaches, whether based on artificial intelligence techniques, control theories, or a combination of both, may exhibit suboptimal performance if they do not incorporate meal intake announcements or meal size information, as suggested by recent studies. o address this limitation, several meal detection algorithms reported in the literature have demonstrated improvements in blood glucose regulation while reducing the occurrence of both hypoglycemic and hyperglycemic events. This paper, therefore, explores supervised learning-based techniques for meal detection and carbohydrate counting. For meal detection, decision boundaries are presented for each algorithm to offer a clearer understanding of their performance. For carbohydrate counting, regression algorithms are trained and evaluated by comparing their predictions to real measured glucose curves within a postprandial time horizon. The algorithms are validated using the FDA-approved UVA/PADOVA Type 1 Diabetes Simulator.