SCH: Leveraging Clinical Time Series to Learn Optimal Treatment of Acute Dyspnea

在正确的时间将正确的患者与正确的治疗快速匹配的能力对于确保患者 接受高质量的护理。医疗保健中的绝大多数机器学习应用都集中在诊断或 根据特定的结果对患者进行分层。相比之下，强化学习(RL)的目的是学习临床状态 (即，一组体征、症状和测试结果)对特定的治疗序列做出反应，目标是 优化临床结果。RL的目的不是诊断，而是根据患者对 具体的治疗--在许多方面模仿临床医生在实践中的操作。这项提议将发展出一部小说 分析电子健康记录(EHR)临床的临床医生在环强化学习(RL)框架 支持医生决策的时间序列数据，反复向医生提供 潜在的治疗策略。我们这项工作的重点是对住院患者的评估和治疗 有急性呼吸困难(呼吸短促)和即将发生呼吸衰竭的迹象。急性呼吸困难是理想的 RL方法的条件，因为它可能是由于三种重叠的条件：充血性心力衰竭、慢性 阻塞性肺病和肺炎。为这些患者确定最佳治疗方案在临床上是困难的， 由于患者的表现常常模棱两可，变化迅速，而且往往是由多种原因引起的。 多达三分之一的患者可能会出现不适当的治疗，从而导致死亡率增加。在开发这一功能时 RL框架，我们还将开发方法来学习更多有用的高维临床表示法 提高RL模型训练效率的时间序列数据。此外，考虑到与 观察健康数据，我们将开发新的方法来评估已学习的政策，并开发新的理论来 使用观测数据更好地理解RL的局限性。该项目有四个目标：1)创建一个可共享的、 未识别的35,000名急性呼吸困难患者的EHR时间序列数据集，2)开发开发技术 在涉及临床时间序列数据的任务中的不变性，以提高RL模型训练的效率；3)开发和 评估基于RL的框架，以了解急性呼吸困难的最佳治疗策略，以及4)前瞻性 验证学习的治疗模型。这项研究将产生学习表示法的新技术 时间序列数据，并将使用观察性临床数据研究RL的理论和实践限制， 导致ML和RL在临床护理方面的关键进步。为临床决策支持开发的工具 提案有可能产生很大的影响，因为它们有能力将这里研究的问题推广到 其他条件，为临床系统奠定基础，通过帮助及时和 对患者进行适当治疗。

The ability to rapidly match the right patients to the right treatments at the right time is critical to ensuring patients receive high quality care. The vast majority of machine learning applications in healthcare focus on diagnosing or stratifying patients for a particular outcome. In contrast, reinforcement learning (RL) aims to learn how clinical states (i.e., sets of signs, symptoms, and test results) respond to specific sequences of treatments, with the goal of optimizing clinical outcomes. RL does not aim to diagnose, but infers diagnosis based on a patient's response to specific treatments--in many ways mimicking how clinicians operate in practice. This proposal will develop a novel clinician-in-the-loop reinforcement learning (RL) framework that analyzes electronic health record (EHR) clinical time-series data to support physician decision making, iteratively providing physicians the estimated outcome of potential treatment strategies. Our topic of focus for this work is the evaluation and treatment of patients hospitalized with acute dyspnea (shortness of breath) and signs of impending respiratory failure. Acute dyspnea is an ideal condition for an RL approach, since it can be due to three overlapping conditions: congestive heart failure, chronic obstructive pulmonary disease and pneumonia. Determining optimal treatment for these patients is clinically difficult, as a patient's presentation is frequently ambiguous, rapidly changing, and often due to multiple causes. Inappropriate treatment may occur in up to a third of patients leading to increased mortality. While developing this RL framework, we will also develop methods to learn more useful representations of high-dimensional clinical time-series data to improve the efficiency of RL model training. In addition, given the challenges of working with observational health data, we will develop new methods for evaluation of learned policies and develop new theory to better understand the limitations of RL using observational data. The project has four aims: 1) create a shareable, de-identified EHR time-series dataset of 35,000 patients with acute dyspnea, 2) develop techniques for exploiting invariances In tasks involving clinical time-series data to improve the efficiency of RL model training, 3) develop and evaluate an RL-based framework for learning optimal treatment policies for acute dyspnea, and 4) prospectively validate the learned treatment model. This research will result in new techniques for learning representations from time-series data and will study both the theoretical and practical limitations of RL using observational clinical data, leading to key advancements in ML and RL for clinical care. The tools developed for clinical decision support in this proposal have the potential for high impact because of their ability to generalize beyond the problem studied here to other conditions, laying the groundwork for clinical systems that directly impact society by aiding in the timely and appropriate treatment of patients.