Population-level Pulmonary Embolism Outcome Prediction with Imaging and Clinical Data: A Multi-Center Study

利用影像学和临床数据预测人群水平的肺栓塞结果：一项多中心研究

• 批准号: 10598324
• 负责人: CURTIS P LANGLOTZ
• 金额: $ 31.06万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10598324
• 关键词: Acute; Address; Algorithms; Area; Award; Biological; Black race; Clinical; Clinical Data; Collaborations; Communication; Computerized Medical Record; Conflict (Psychology); Consensus; Data; Decision Making; Development; Diagnostic Imaging; Environment; Ethical Issues; Ethics; Ethnic Origin; Fostering; Future; Goals; Healthcare; Human; Image; Individual; Information Systems; Institution; Intelligence; Interview; Investigation; Label; Laboratories; Latinx; Leg; Life Cycle Stages; Measures; Medicaid; Methodology; Methods; Modeling; Multicenter Studies; Nonmaleficence; Outcome; Outcome Measure; Output; Parents; Patient-Focused Outcomes; Patients; Performance; Population; Process; Pulmonary Embolism; Pulmonology; Race; Radiology Specialty; Recommendation; Research; Research Personnel; Resolution; Risk; Sensitivity and Specificity; Series; Site; Structure; Subgroup; System; Testing; Thoracic Radiography; Training; Underserved Population; Work; base; computer science; deep learning; deep learning model; design; effectiveness testing; experience; health record; learning strategy; mortality; outcome prediction; point of care; predictive modeling; pressure; risk stratification; sex

Project Summary The goal of the parent award is to develop an automated healthcare AI (AI-HC) to achieve point-of-care risk stratification for pulmonary embolism (PE) patient outcomes using a fusion deep learning strategy that can simultaneously analyze health records and imaging data. An ideal PE risk-scoring system would not only predict mortality, but also assess the risk for the many debilitating long-term consequences of acute PE. Such a system would, therefore, facilitate optimal management and would likely require intelligent use of clinical, laboratory, and imaging data together in order to provide accurate patient -specific risk scoring for multiple PE outcome measures. In order to build a robust model, the parent award will apply distributed training of deep learning models across four large US healthcare institutions. Distributing the algorithm rather than the data avoids sharing individually identifiable patient information. If successful, this parent award will be the first endeavor to leverage diagnostic imaging (pixel) data in combination with structured and unstructured electronic medical record (EMR) data to predict outcomes. Using a powerful combination of clinical, laboratory, and imaging data, this system will provide patient-specific risk scoring for multiple PE outcome measures. Further, the parent award fosters multi- center collaborations, including investigation of the generalizability of the approach to different populations of PE patients and to train, test, and ultimately deploy the automated predictive model in a variety of clinical environments. Partnering with the parent award presents a unique opportunity to address two pressing ethical questions: How do you anticipate, identify, and address ethical problems with AI-HC before they cause harm? How do you document and communicate important ethical constraints with AI-HC, once identified, to multiple users (including the developers of the AI)? The supplement team has worked closely with the parent award investigators on ethics of AI-HC generally and on developing approaches to examine AI-HC. In this supplement we will pilot an approach to: 1) identify ethical issues that may emerge with development and multi-site deployment of AI-HC for PE; and 2) develop consensus on how to address these ethical issues. We will also 3) develop consensus on an ethics “label” to communicate identified and addressed ethical constraints. In doing 1, 2 & 3 we will refine a generalizable approach for identifying and addressing ethical challenges with an AI-HC and a roadmap for how to communicate identified ethical concerns for AI-HC.

项目摘要 父母奖的目标是开发自动化医疗保健AI（AI-HC）以实现护理风险 使用融合深度学习策略的肺栓塞分层（PE）患者结局 类似地分析健康记录和成像数据。理想的PE计分数系统不仅将 预测死亡率，同时还可以评估急性PE的许多长期后果的风险。这样的 因此，系统将有助于最佳管理，并且可能需要智能地使用临床， 实验室和成像数据一起为多个PE提供精确的患者特异性风险评分 结果措施。为了建立强大的模型，父母奖将应用深度的分布式培训 在美国四个大型医疗机构中学习模型。分发算法而不是数据 避免共享单独识别的患者信息。如果成功，则该父母奖将是第一个 努力利用诊断成像（像素）数据与结构化和非结构化电子结合 病历（EMR）数据以预测结果。结合临床，实验室和 成像数据，该系统将为多种PE结果指标提供特定于患者的风险评分。更远， 家长奖促进了多中心合作，包括调查 采用不同人群的PE患者并培训，测试和最终部署自动化的方法 各种临床环境中的预测模型。 与父母奖合作提供了一个独特的机会，可以解决两个紧迫的道德问题： 您是否在AI-HC造成伤害之前会期待，识别和解决道德问题？你怎么样 将重要的道德约束与AI-HC传达给多个用户 （包括AI的开发人员）？补充团队已与父母奖密切合作 关于AI-HC伦理的研究人员通常以及开发检查AI-HC的方法。在这个 补充我们将试用一种方法：1）确定可能随着发展而出现的道德问题 PE的AI-HC多站点部署； 2）就如何解决这些道德问题建立共识。我们 3）也将在道德“标签”上建立共识，以交流已确定的伦理 约束。在1、2和3时，我们将完善一种可识别和解决道德的可推广方法 AI-HC和路线图的挑战是如何传达AI-HC的确定道德问题。