Exploration of Foundational Models for Blood Glucose Forecasting in Type-1 Diabetes Pediatric Patients

Aims: The accurate prediction of blood glucose (BG) levels is critical for managing Type-1 Diabetes (T1D) in pediatric patients, where variability due to factors like physical activity and developmental changes presents significant challenges. Methods: This work explores the application of foundational models, particularly the encoder–decoder model TimeGPT, for BG forecasting in T1D pediatric patients. Methods: The performance of TimeGPT is compared against state-of-the-art models, including ARIMAX and LSTM, and multilayer perceptron (MLP) architectures such as TiDE and TSMixer. The models were evaluated using continuous glucose monitoring (CGM) data and exogenous variables, such as insulin intake. Results: TimeGPT outperforms or achieves comparable accuracy to the state of the art and MLP models in short-term predictions (15 and 30 min), with most predictions falling within the clinically safe zones of the Clarke Error Grid. Conclusions: The findings suggest that foundational models like TimeGPT offer promising generalization capabilities for medical applications and can serve as valuable tools to enhance diabetes management in pediatric T1D patients.