Reinforcement Learning in Healthcare: Applications for Personalized Treatment Planning and Clinical Decision Support
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Abstract
Reinforcement Learning (RL) has gained increasing attention in healthcare for its potential to personalize treatment planning and clinical decision support. an overview of RL techniques and their applications in healthcare settings. RL algorithms, such as Q-learning and deep Q-networks, enable agents to learn optimal treatment policies by interacting with patients or simulating clinical scenarios. RL has been applied to a variety of healthcare tasks, including personalized treatment planning for chronic diseases, adaptive clinical trial design, and resource allocation in healthcare systems. By incorporating patient preferences, clinical guidelines, and real-time data, RL can support clinicians in making more informed and personalized treatment decisions. However, challenges such as data heterogeneity, interpretability, and ethical considerations must be addressed to facilitate the widespread adoption of RL in healthcare. his paper discusses these challenges and highlights potential future directions for research and application of RL in personalized treatment planning and clinical decision support within healthcare settings.
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