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Sepsis is a leading cause of death in the ICU. It is a disease requiringcomplex interventions in a short period of time, but its optimal treatmentstrategy remains uncertain. Evidence suggests that the practices of currentlyused treatment strategies are problematic and may cause harm to patients. Toaddress this decision problem, we propose a new medical decision model based onhistorical data to help clinicians recommend the best reference option forreal-time treatment. Our model combines offline reinforcement learning and deepreinforcement learning to solve the problem of traditional reinforcementlearning in the medical field due to the inability to interact with theenvironment, while enabling our model to make decisions in a continuousstate-action space. We demonstrate that, on average, the treatments recommendedby the model are more valuable and reliable than those recommended byclinicians. In a large validation dataset, we find out that the patients whoseactual doses from clinicians matched the decisions made by AI has the lowestmortality rates. Our model provides personalized and clinically interpretabletreatment decisions for sepsis to improve patient care.

Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space