论文标题
生存的强化学习，这是一种临床动机的重症患者的临床动机方法
Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients
论文作者
论文摘要
直接从观察数据中直接利用RL和随机控制方法来学习针对重症患者的最佳治疗策略的兴趣很大。但是，对于标准RL目标，控制目标和最佳奖励选择存在明显的歧义。在这项工作中，我们提出了一个重症患者的临床动机控制目标，该价值功能具有简单的医学解释。此外，我们提出理论结果并将我们的方法调整为实用的深度RL算法，该算法可与任何基于价值的深度RL方法一起使用。我们在大型败血症队列上进行实验，并表明我们的方法与临床知识一致。
There has been considerable interest in leveraging RL and stochastic control methods to learn optimal treatment strategies for critically ill patients, directly from observational data. However, there is significant ambiguity on the control objective and on the best reward choice for the standard RL objective. In this work, we propose a clinically motivated control objective for critically ill patients, for which the value functions have a simple medical interpretation. Further, we present theoretical results and adapt our method to a practical Deep RL algorithm, which can be used alongside any value based Deep RL method. We experiment on a large sepsis cohort and show that our method produces results consistent with clinical knowledge.
