Safe Learning of Adaptive Control Policies for Remote Patient Monitoring
The research develops a learning-based control framework for Remote Patient Monitoring (RPM). It uses reinforcement learning to estimate unknown system parameters and adapt monitoring policies in real time. The goal is to balance patient safety against monitoring costs by finding an optimal threshold policy that minimizes expected discounted costs.