Drug Dosage Control System Using Reinforcement Learning

Abstract

This project introduces a pioneering approach for optimizing drug dosage control strategies through the utilization of reinforcement learning (RL), a sophisticated subset of machine learning techniques. The core objective is to dynamically adjust drug dosages in real-time based on patient responses, thereby maximizing therapeutic efficacy while minimizing potential adverse effects. By integrating reinforcement learning algorithms, including Q-learning, Deep Q-Networks (DQN), and actor-critic methods, the system learns from patient data to make precise dosage adjustments considering individual patient characteristics, disease progression, and response to treatment. The framework promises to revolutionize personalized medicine by providing tailored drug dosages, enhancing treatment outcomes, and ensuring patient safety. The project's scope covers not only the development and implementation of this innovative RL- based system but also addresses significant challenges such as model interpretability, scalability, and regulatory compliance, ensuring its practical applicability in healthcare settings. Through this work, we aim to bridge the gap between conventional drug prescription methodologies and the potential for personalized, optimized care, making a substantial contribution to the advancement of healthcare systems.