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.
@article{arxiv.2103.05612,
title = {Challenges for Reinforcement Learning in Healthcare},
author = {Elsa Riachi and Muhammad Mamdani and Michael Fralick and Frank Rudzicz},
journal= {arXiv preprint arXiv:2103.05612},
year = {2021}
}