Enhance differential privacy mechanisms for clinical data analysis using CNNs and reinforcement learning

This work proposes a novel approach that provides a comparative reinforcement learning framework for clinical decision-making in critical care situations. The primary emphasis is on predicting and optimizing ventilation and sedation strategies for patients in Intensive Care Units. We safeguard patient data in the MIMIC-III dataset while preserving analytical utility by integrating differential privacy techniques, such as Gaussian, Laplace, Exponential, Zero-Concentrated, and Rényi mechanisms. We investigated the synergy between Convolutional Neural Networks (CNNs) and two reinforcement learning algorithms: the value-based Deep Q-Network (DQN) and the policy-based Advantage Actor-Critic (A2C). The proposed framework assesses both models in various privacy-protective environments, providing comprehensive information on how distinct differential privacy techniques affect learning stability, accuracy, and clinical applicability. The performance was evaluated using established metrics including Accuracy, Precision, Recall, F1-Score, and mean absolute error (MAE). The results demonstrate that DQN performs well under most privacy settings, and A2C performs better in certain configurations, which indicates the need to match the RL strategy with specific privacy characteristics. This thorough empirical analysis provides a basis for designing healthcare systems that incorporate privacy considerations into reinforcement learning.