Glucose Prediction

Glucose prediction aims to accurately forecast future blood glucose levels, primarily to improve diabetes management and prevent hypo- or hyperglycemic events. Current research emphasizes developing personalized and privacy-preserving models using diverse data sources (continuous glucose monitors, dietary intake, insulin doses) and advanced architectures like transformers, recurrent neural networks, and federated learning approaches. These efforts focus on enhancing prediction accuracy, particularly in challenging scenarios like glycemic excursions, while simultaneously providing interpretable results and ensuring patient data privacy, ultimately leading to better clinical decision-making and improved patient outcomes.