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Title:: Research on medication strategy of sepsis based on reinforcement learning

Author(s):: WANG Deyong; ZHANG Tianyi; CHENG Yunzhang; （Shanghai Engineering Research Center of Interventional Medical Devices,University of Shanghai for Science and Technology, Shanghai 200093, China）

Keywords:: Sepsis; Treatment strategies; Vasopressors; Intravenous fluids; Reinforcement learning; Value iteration; Policy iteration

摘要:: 为解决难以为脓毒症患者提供针对性治疗方案的问题，本研究基于MIMIC重症监护数据，利用强化学习中的价值迭代和策略迭代方法，求解了两套静脉输液和血管升压药用药策略。结果表明，由模型所得策略在不同患者轨迹上求得的平均累计回报要明显高于临床医生的用药策略。本研究可为临床医生制定脓毒症治疗策略提供决策参考，在临床脓毒症用药指导上具有广阔应用前景。

Abstract:: Aim to the difficulty of providing targeted treatment programs for sepsis patients, we used value iteration and strategy iteration methods in reinforcement learning to solve two sets of intravenous fluid volume and vasopressor medication strategy based on MIMIC data. The results showed that the average cumulative return obtained from the strategies based on the model was significantly higher than that obtained from the medication strategies of clinicians on different patient tracks. It can provide reference for clinicians to formulate treatment strategies for sepsis, this method has a broad application prospect in the medication guidance of clinical sepsis.