Application of Reinforcement Learning for the Development of Precision Medicine Treatment - R. Meenakshi; Subbiah Murugan; Rajamanickam Sivaranjani; Jayavarapu Karthik; T. Merlin Inbamalar; Jayabharathi Ramasamy; K. Umapathy (International Journal of Computational and Experimental Science and Engineering, 2025)
The goal of this presentation is to explain the purpose, target, ambition, and effect of the employment of Reinforcement Learning (RL) methods in the process of developing tailored treatment plans within the application of precision medicine. The objective is to improve the results for patients by adapting medical procedures to the specific features and requirements of each individual patient. It is the goal of RL to improve treatment options by iteratively learning from patient reactions and updating suggestions in accordance with those learnings. The contribution consists of expanding the area of precision medicine via the use of RL algorithms, which provide a framework for treatment optimization that is both dynamic and flexible. The use of patient data, which may include genetic profiles, biomarkers, and clinical histories, enables RL to assist the production of individualized therapies that consider individual variability and response patterns. The use of this strategy has the potential to revolutionize the practice of medicine by ushering in a new age of individualized therapies that are customized to the specific features of each individual patient. It establishes a foundation for future research and the application of decision support systems based on reinforcement learning in clinical settings, which will eventually lead to improvements in patient outcomes and the delivery of healthcare.