Challenges for Reinforcement Learning in Healthcare
It identifies key barriers for deploying RL in real-world healthcare settings, making it an incremental analysis rather than a solution.
The paper addresses the application of reinforcement learning to optimize sequential treatment decisions in healthcare, highlighting challenges like reward specification and policy evaluation that hinder its practical use.
Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome. Such optimization problems may be amenable to reinforcement learning. A reinforcement learning agent could be trained to provide treatment recommendations for physicians, acting as a decision support tool. However, a number of difficulties arise when using RL beyond benchmark environments, such as specifying the reward function, choosing an appropriate state representation and evaluating the learned policy.