Personalized Diabetes Management and Treatment Planning using Reinforcement Learning

Diabetes management in healthcare is a complex and ongoing challenge, demanding personalized approaches that adapt to individual patient needs. Traditional methods often fall short in delivering customized solutions that account for the diverse physiological and lifestyle factors of each patient. Reinforcement Learning (RL), a subset of artificial intelligence, presents a promising frontier for developing tailored strategies in diabetes treatment planning. This paper investigates the application of RL techniques specifically within the context of diabetes management. By leveraging RL’s ability to learn and adapt based on patient interactions and feedback, novel approaches to treatment planning can be explored. The focus is on enhancing the efficacy and personalization of diabetes care, aiming to improve patient outcomes and quality of life through adaptive and responsive management strategies. This research contributes to advancing the understanding and implementation of AI-driven solutions in healthcare, particularly in the domain of chronic disease management like diabetes.