Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning.
Journal:
IEEE transactions on bio-medical engineering
PMID:
39208051
Abstract
OBJECTIVE: The artificial pancreas (AP) shows promise for closed-loop glucose control in type 1 diabetes mellitus (T1DM). However, designing effective control policies for the AP remains challenging due to complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but struggles with distribution shifts and substantial data requirements.