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.

项目总结