Accurate blood glucose level (BGL) forecasting is critical for diabetes self-management and clinical decision-making. Although deep learning models based on continuous glucose monitoring (CGM) data have achieved encouraging results, most approaches rely exclusively on historical observations and cannot explicitly account for future disturbances, such as insulin delivery and meal intake, that are unavailable at deployment. To address this limitation, we propose a future-aware learning framework for multi-step BGL prediction that leverages privileged information during training while preserving deployability at inference. A Transformer-based teacher model is trained offline using both historical CGM data and future disturbance information to learn disturbance-aware temporal representations. A student model with a similar sequence-to-sequence structure is then trained using knowledge distillation to approximate the teacher's representations based solely on historical inputs, enabling real-time forecasting without access to future data. The proposed framework is evaluated on the publicly available OhioT1DM and AZT1D datasets for prediction horizons ranging from 30 to 120 minutes and compared with several established methods. The results show consistent reductions in root mean squared error and mean absolute error, together with improved clinical reliability as assessed by Clarke error grid analysis, with over 90% of predictions falling within clinically acceptable regions. These findings demonstrate the potential of future-aware training strategies to enhance glucose forecasting performance under realistic deployment constraints.
Sun et al. (Sat,) studied this question.