Metabolites
Tail-Risk-Aware Model Predictive Control for Hypoglycemia Mitigation in Automated Insulin Delivery
Par Kaiyuan Zhang, Congyu Han, Hexing Guo et 5 autres.
Contenu en anglais
Résumé
Background
Automated insulin delivery must maintain glycemic control despite partial observability, physiological heterogeneity, and sensing and actuation errors. Cohort-average metrics may nevertheless conceal hypoglycemia in vulnerable virtual patients.
Methods
We developed a tail-risk-aware model predictive control framework that combines a Transformer-based latent world model, unguided iterative cross-entropy method planning over perturbed rollouts, and a deterministic Safety Layer that projects insulin actions onto programmed bounds. Reinforcement-learning-guided proposal sampling was evaluated separately as an exploratory search variant. The primary controller was evaluated in Simglucose using 10 virtual patients, three random seeds, and five prespecified operating conditions.