Predicting Adverse Events for Patients with Type-1 Diabetes Via Self-Supervised Learning

Predicting blood glucose levels is fundamental for precise primary care of type-1 diabetes (T1D) patients. However, it is challenging to predict glucose levels accurately, not to mention the early alarm of adverse events (hyperglycemia and hypoglycemia), namely the minority class. In this paper, we propose BG-BERT, a novel self-supervised learning framework for blood glucose level prediction. In particular, BG-BERT incorporates masked autoencoder to capture rich contextual information of blood glucose records for accurate prediction. More specifically, SMOTE data augmentation and shrinkage loss are employed to effectively handle adverse events without discrimination. We evaluate BG-BERT on two benchmark datasets against two state-of-the-art base-line models. The experimental results highlight the significant improvements achieved by BG-BERT in glucose level prediction accuracy (measured by RMSE) and sensitivity to adverse events, with average lifting ratios of 9.5% and 44.9%, respectively.