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

SCH：利用临床时间序列学习急性呼吸困难的最佳治疗方法

• 批准号: 10015336
• 负责人: Michael William Sjoding
• 金额: $ 23.52万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10015336
• 关键词: Acute; Address; Affect; Algorithms; Blood Pressure; Chronic Obstructive Airway Disease; Clinical; Clinical Data; Computer Vision Systems; Congestive Heart Failure; Data; Data Set; Decision Making; Diagnosis; Drops; Dyspnea; Electronic Health Record; Engineering; Ensure; Evaluation; Event; Goals; Health; Healthcare; Hospitalization; Hospitals; Image; Infrastructure; Learning; Linear Models; Machine Learning; Methods; Modeling; Morality; Outcome; Pathologic Processes; Patient Rights; Patient-Focused Outcomes; Patients; Performance; Physicians; Pneumonia; Policies; Positioning Attribute; Protocols documentation; Psychological reinforcement; Quality of Care; Research; Research Personnel; Respiratory Failure; Rewards; Right to Treatments; Sample Size; Sampling; Selection for Treatments; Series; Shortness of Breath; Signal Transduction; Signs and Symptoms; Societies; Stream; Symptoms; System; Techniques; Test Result; Theoretical Studies; Time; Training; Variant; Work; base; clinical care; clinical database; clinical decision support; clinical practice; clinically relevant; convolutional neural network; deep learning; deep neural network; design; health data; high dimensionality; improved; innovation; interdisciplinary collaboration; learning algorithm; learning strategy; mortality; network architecture; novel; optimal treatments; patient response; patient stratification; prospective; success; supervised learning; support tools; theories; tool; treatment strategy

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.

在正确的时间迅速将正确患者与正确治疗相匹配的能力对于确保患者至关重要 获得高质量的护理。医疗保健中绝大多数机器学习应用都集中在诊断或 对患者进行特定结果进行分层。相比之下，增强学习（RL）旨在了解临床状态 （即，一组体征，症状和测试结果）应对特定的治疗序列，目的是 优化临床结果。 RL的目的不是诊断，而是根据患者对 特定的治疗方法 - 通过许多方式模仿临床医生在实践中的运作方式。该提议将发展一部小说 分析电子健康记录（EHR）临床的临床医生在循环增强学习（RL）框架 时间序列数据支持医师决策，迭代地为医师提供了估计的结果 潜在的治疗策略。这项工作的重点主题是对住院的患者的评估和治疗 急性呼吸困难（呼吸急促）和即将发生的呼吸衰竭迹象。急性呼吸困难是理想的 RL方法的条件，因为这可能是由于三个重叠的条件：充血性心力衰竭，慢性 阻塞性肺部疾病和肺炎。确定这些患者的最佳治疗在临床上很难 由于患者的表现经常模棱两可，迅速变化，并且通常是由于多种原因。 最多三分之一的患者可能会发生不当治疗，导致死亡率增加。在开发此过程的同时 RL框架，我们还将开发方法来学习高维临床的更多有用表示 时间序列数据以提高RL模型培训的效率。此外，考虑到与之合作的挑战 观察健康数据，我们将开发新的方法来评估学习政策，并开发新的理论 更好地了解使用观察数据的RL的局限性。该项目有四个目标：1）创建一个可共享的， 35,000例急性呼吸困难患者的去识别的EHR时间序列数据集，2）开发用于利用的技术 涉及临床时间序列数据以提高RL模型培训效率的任务中的不变性，3）开发和 评估基于RL的框架，用于学习急性呼吸困难的最佳治疗政策，4）前瞻性 验证学习的治疗模型。这项研究将为学习表征提供新的技术 时间序列数据，并将使用观察性临床数据研究RL的理论和实际局限性， 导致ML和RL的关键进步用于临床护理。为此开发的用于临床决策支持的工具 提案具有高影响力的潜力，因为它们有能力超越此处研究的问题 其他条件，为临床系统奠定了基础，这些系统通过及时和 适当的患者治疗。