Integrating differential privacy in deep reinforcement learning for sepsis treatment with pulmonary implications

Abstract

Pulmonary diseases, such as pneumonia and lung abscess, can trigger sepsis, while sepsis-induced immune dysfunction exacerbates Pulmonary tissue damage, creating a vicious cycle. Therefore, designing a safe and effective clinical treatment planning method for sepsis is critically significant. In recent years, deep reinforcement learning (DRL), as one of the artificial intelligence technologies, has achieved remarkable results in the field of sepsis treatment. However, DRL models may be attacked due to their sensitive training data and their high commercial value, especially with the increasing number of DRL models being released on the Internet. Consequently, protecting the "privacy" of DRL models and training data has become an urgent problem. To address this issue, we propose a differential privacy-based DRL model for sepsis treatment. Furthermore, we investigate the impact of differential privacy mechanisms on the performance of the DRL model. Experimental results demonstrate that integrating differential privacy into DRL models enables clinicians to design sepsis treatment plans while protecting patient privacy, thereby mitigating lung tissue damage and dysfunction caused by sepsis.

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