Optimization Techniques in Reinforcement Learning for Healthcare: A Review

One paradigm for machine learning that is transforming is reinforcement learning, or RL, promising significant healthcare improvements through personalized treatment recommendations, optimized resource allocation, and enhanced predictive modeling. Despite these successes, challenges such as model interpretability and real-world integration persist. This paper reviews various optimization techniques employed in RL, focusing on Proximal Policy Optimization (PPO) due to its stability, efficiency, and applicability in healthcare. Applications of PPO include adaptive treatment strategies, personalized medication management, and optimized clinical workflows. Additionally, the paper discusses case studies demonstrating RL’s potential to reduce medication errors, enhance treatment protocols, predict adverse events, and personalize radiation therapy. A comprehensive database search initially retrieved 708 articles; after removing duplicates and screening based on titles and abstracts, 23 articles were considered, ultimately narrowing down to 15 articles following the application of exclusion criteria. The findings demonstrate RL’s significant impact on healthcare, enhancing treatment effectiveness, optimizing service selection, and improving patient care through advanced techniques like Deep RL and multi-agent systems. Future research should focus on developing sophisticated adaptive treatment models and validating RL applications through clinical trials, underscoring RL’s potential to revolutionize healthcare by making it more effective, efficient, and patient centric.