Reinforcement Learning with Balanced Clinical Reward for Sepsis Treatment

Sepsis, a severe reaction to infection, presents significant challenges in intensive care units (ICUs), often resulting in high mortality rates. Traditional treatment approaches, primarily reliant on clinicians’ judgment and standard guidelines, frequently fail to deliver personalized care. Moreover, clinical decisions may vary considerably among healthcare providers managing identical patient cases. In this study, we propose an innovative method for optimizing sepsis treatment strategies through Deep Reinforcement Learning (DRL), leveraging patient data, medical expertise, and comprehensive sepsis research. Additionally, we develop an interpretable reward formulation to guide the DRL agent in learning from real clinical data, aiming to enhance treatment outcomes and mitigate mortality risks. Our results demonstrate that the DRL approach surpasses existing methods, leading to safer sepsis treatment decisions and correlating with increased patient survival rates. This investigation underscores the potential of Artificial Intelligence (AI) in enhancing treatments for sepsis and other intricate medical conditions.