Reinforcement Learning-Based Adaptive Therapy Planning for Personalized Healthcare Applications

Abstract

Individualized healthcare requires dynamic treatment plans which can address the changing conditions of patients and complicated clinical dynamics. The sequential, uncertain, and long-term nature of therapeutic decision-making is constrained in conventional rule-based systems, and in simple predictive models, which rely on short-term predictive statistics. This research suggests a reinforcement learning (RL)-based model of adaptive therapy planning, a Markov Decision Process (MDP) to represent the dynamics of patient states and the treatment actions with time. The states of patients are being built on the basis of longitudinal electronic health record (EHR) data, which includes vital signs, laboratory measurements, and clinical history as well as actions are discrete therapeutic decisions. A Deep Q-Network (DQN) is used to find the best policy of treatment to maximize cumulative clinical good via a reward function that combines the survival outcome, reduction of risk, and effectiveness of treatment. The framework is tested on a benchmark critical care dataset, compared to conventional supervised learning models, such as logistic regression, random forests and LSTM-based methods. Empirical evidence has shown that the RL-based model has a higher success rate, cumulative reward is improved by around 1520 percent and adaptability to patient-specific trajectories is improved. These results emphasize the usefulness of RL in streamlining serial clinical decision-making and clarify its possibilities to be included in the intelligent clinical decision support systems to deliver personalized healthcare.