论文标题
用苏打-RL确定急性低血压的独特，有效的治疗方法：安全优化多样的精确增强学习
Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning
论文作者
论文摘要
重症监护环境中的低血压是威胁生命的紧急情况，必须早日认可和治疗。虽然液体推注疗法和加压剂是常见的治疗方法，但通常不清楚要进行哪些干预措施，含量和多长时间。电子健康记录形式的观察数据可以提供一个来源，以帮助从过去的事件中获取这些选择，但是通常不可能仅凭观察数据就可以确定单一的最佳策略。在这种情况下，我们认为将合理选项的收集汇总给提供商很重要。为此，我们开发了苏打水：安全优化，多样化和准确的增强学习，以识别数据中支持的不同治疗方案。我们在10,142个ICU的队列上演示了苏打水，在出现低血压的情况下。我们博学的政策与观察到的医师行为相当，同时为治疗决策提供了不同的合理替代方案。
Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early. While fluid bolus therapy and vasopressors are common treatments, it is often unclear which interventions to give, in what amounts, and for how long. Observational data in the form of electronic health records can provide a source for helping inform these choices from past events, but often it is not possible to identify a single best strategy from observational data alone. In such situations, we argue it is important to expose the collection of plausible options to a provider. To this end, we develop SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning, to identify distinct treatment options that are supported in the data. We demonstrate SODA-RL on a cohort of 10,142 ICU stays where hypotension presented. Our learned policies perform comparably to the observed physician behaviors, while providing different, plausible alternatives for treatment decisions.
