Advancing COPD Diagnosis with Hierarchical Deep Q-Networks: A Reinforcement Learning Approach

Chronic Obstructive Pulmonary Disease (COPD) is a progressive and debilitating respiratory disorder that poses a significant burden on global health. Early and accurate diagnosis is vital for effective management and treatment, yet current diagnostic methods often involve complex, resource-heavy processes that can delay care. A novel solution to this challenge is the application of Hierarchical Deep Q Networks (H-DQN), an advanced reinforcement learning (RL) technique designed to streamline the diagnostic process.This system mirrors clinical decision-making, where subsequent tests or evaluations depend on the results of earlier ones. It enables a more efficient, goal-directed approach to diagnosis, reducing the time and resources spent on unnecessary tests. The RL environment is tailored to simulate the diagnostic journey by integrating various patient data, including demographics, medical history, and test results. The reward function is designed to optimize diagnostic accuracy while minimizing the need for extraneous procedures, thus improving both efficiency and cost-effectiveness. Results from experimental applications show that H-DQN significantly outperforms traditional methods in terms of both accuracy and efficiency. Moreover, the hierarchical decision-making structure provides a clear rationale for each diagnostic action, enhancing the interpretability of the system. This not only facilitates adoption in clinical settings but also ensures that the system remains transparent and aligned with medical standards. By leveraging reinforcement learning, this approach promotes the identification of hidden patterns in patient data, paving the way for more personalized, data-driven healthcare solutions.