Saman Khamesian, Asiful Arefeen, Maria Adela Grando et al.
Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avoid the harmful effects of dysglycemia, including both hyperglycemia and hypoglycemia. Despite the development of advanced technologies such as automated insulin delivery (AID) systems, achieving optimal glycemic control remains challenging. AID systems combine continuous subcutaneous insulin infusion with data from continuous glucose monitors (CGMs), offering potential benefits in reducing glucose variability and increasing time-in-range. However, these systems still frequently fail to prevent dysglycemia, partly due to limitations in their prediction algorithms, which lack the accuracy needed to avert abnormal glucose events. This shortcoming highlights the need for more advanced glucose forecasting methods. To address this need, we introduce GLIMMER, Glucose Level Indicator Model with Modified Error Rate, a machine learning-based model for predicting blood glucose levels. GLIMMER classifies glucose values into normal and abnormal ranges and employs a novel custom loss function that prioritizes accuracy in dysglycemic regions, where patient safety is most critical. To evaluate GLIMMER's effectiveness for T1D management, we used both a publicly available dataset and a newly collected dataset involving 25 individuals with T1D. In forecasting glucose levels for the next hour, GLIMMER achieved a root mean square error (RMSE) of 23.97 (+/-3.77) and a mean absolute error (MAE) of 15.83 (+/-2.09) mg/dL. These results represent a 23% improvement in RMSE and a 31% improvement in MAE compared to the best previously reported models.