Optimizing Clinical Decision-Making Using Q-Learning: A Reinforcement Learning Approach to Patient Treatment Policies

Abstract

Reinforcement learning is booming in the healthcare industry by the grip that it has over the DMPs on its way to realizing personalized treatment strategies. The paper is about the way the Q-learning model-free RL can optimize treatment policies across the spectrum of patient health states, from Healthy to Critical. The proposed algorithm framed a Markov Decision Process (MDP) in terms of health states, actions, transition probabilities, and rewards, based on simulated outcomes along the trajectory of patients involved in the study. Positive reinforcement was defined to include healthy to less healthy transitions and penalizing healthy to less healthy changes, reverting to the reward function source already designed for every transition. To balance exploration on exploitation, the agent follows an epsilon-greedy policy, which gradually builds up simulated experiences to learn its own policies. The total cumulative reward, rolling average return, final state distribution, and frequency of actions taken are the metrics of measurement used by the proposed system. Learning was observed from the results that show the number of agents in favor of aggressive therapy for patients with serious states and low-key during health, as generally understood within the context of modern medicine. This was further enhanced with interpretation by visualization techniques such as heatmaps and bar charts, bringing clinical validity to this step towards RL-based decision-making systems. In conclusion, we show how RL in general and Q-learning in particular can serve as foundations for today's responsive, efficient, and interpretable decision support systems in healthcare.

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