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

基本信息

批准号：
10687126
负责人：
CURTIS P LANGLOTZ
金额：
$ 41.44万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10687126
关键词：
Acute Algorithms Angiography Anticoagulant therapy Architecture Biological Markers Blood Vessels Cardiogenic Shock Caring Cause of Death Chest imaging Clinical Clinical Data Code Collaborations Computational Technique Computerized Medical Record Computers Computing Methodologies Data Data Analytics Data Set Diagnosis Diagnostic Diagnostic Imaging Engineering Environment Evaluation Exhibits Fostering Funding Goals Health Care Costs Health Insurance Portability and Accountability Act Healthcare Heart Arrest Hemorrhage Hospitals Image Individual Information Systems Institution Intelligence Laboratories Learning Lung Medical Imaging Medicine Methods Modeling Morbidity - disease rate Multicenter Studies National Heart, Lung, and Blood Institute Outcome Outcome Measure Outpatients Pathology Report Patient-Focused Outcomes Patients Pharmaceutical Preparations Population Pulmonary Embolism Pulmonary Hypertension Radiology Specialty Recurrence Research Resuscitation Risk Risk Assessment Scanning Sensitivity and Specificity Severities Site Source Statistical Models Structure Syndrome System Testing Therapeutic Time Training United States United States National Library of Medicine X-Ray Computed Tomography breast imaging care delivery chronic thromboembolic pulmonary hypertension clinical care clinical predictors data de-identification data repository deep learning deep learning model demographics design digital medicine electronic structure experience health care quality health data health record heterogenous data high risk hospital readmission imaging biomarker imaging study improved improved outcome indexing learning strategy mortality mortality risk neuroimaging outcome prediction patient safety patient stratification point of care precision medicine predictive modeling primary outcome prognostic model prospective radiological imaging risk stratification secondary outcome stem

项目摘要

Project Summary Pulmonary embolism (PE) is a leading cause of death in the United States. Risk stratification for acute PE treatment can reduce mortality. Risk scoring systems use clinical and laboratory electronic medical record (EMR) data. In addition, biomarkers on computed tomography imaging can identify which patients with PE are at high risk of death, independent of clinical data. Despite advances in clinical and image-driven scoring systems, improving outcomes in acute PE depends on implementation of patient-specific EMR and imaging data analytic prognostic models at the point of care. The promise of digital medicine stems in part from the hope that by digitizing health data, we can leverage computer information systems to understand and improve care. A method that can make use of these data to predict patient-specific outcomes could not only provide major benefits for patient safety and healthcare quality but also reduce healthcare costs. Unfortunately, most of this information is not yet included in predictive statistical models that clinicians use to improve care delivery. This is because traditional computational methods and techniques are insufficient at accurately analyzing such high volumes of heterogeneous data. The goal of this proposal is to develop an automated precision medicine approach to achieve point-of-care risk stratification for 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, we propose to apply distributed training of deep learning models across four large US healthcare institutions. By distributing the algorithm rather than the data, we avoid sharing individually identifiable patient information. If successful, this project will be the first endeavor to leverage diagnostic imaging (pixel) data in combination with structured and unstructured EMR data to predict outcomes. We have the ideal research team, experience, and methods to develop an automated risk-scoring system for acute PE patients. Using a powerful combination of clinical, laboratory, and imaging data, this system will provide patient-specific risk scoring for multiple PE outcome measures. Further, this project will foster multi- center collaborations, which will afford us the opportunity to investigate the generalizability of our approach to different populations of PE patients and to train, test, and ultimately deploy our automated predictive model in a variety of clinical environments.

项目概要肺栓塞（PE）是美国的一个主要原因。急性肺栓塞的危险分层治疗可以降低死亡率。风险评分系统使用临床和实验室电子病历（电子病历）数据。此外，计算机断层扫描成像的生物标志物可以识别哪些 PE 患者是死亡风险很高，与临床数据无关。尽管临床和图像驱动评分取得了进步系统，改善急性肺栓塞的结果取决于患者特异性 EMR 和成像的实施护理点数据分析预后模型。数字医学的前景部分源于希望通过数字化健康数据，我们可以利用计算机信息系统来理解和改善护理。可以利用这些数据的方法预测患者特定的结果不仅可以为患者安全和医疗质量带来重大好处还可以降低医疗费用。不幸的是，大部分信息尚未包含在预测中临床医生用来改善护理服务的统计模型。这是因为传统计算方法和技术不足以准确分析如此大量的异构数据。该提案的目标是开发一种自动化精准医疗方法，以实现护理点风险使用可以同时分析的融合深度学习策略对 PE 患者结果进行分层健康记录和影像数据。理想的 PE 风险评分系统不仅可以预测死亡率，还可以预测死亡率评估急性肺栓塞造成许多使人衰弱的长期后果的风险。这样的系统将，因此，促进最佳管理，可能需要明智地使用临床、实验室和将成像数据结合在一起，以便为多种 PE 结果提供准确的患者特定风险评分措施。为了构建鲁棒的模型，我们建议应用深度学习模型的分布式训练横跨美国四家大型医疗机构。通过分发算法而不是数据，我们可以避免共享可识别个人身份的患者信息。如果成功的话，这个项目将是第一个尝试利用诊断成像（像素）数据与结构化和非结构化 EMR 数据相结合进行预测结果。我们拥有理想的研究团队、经验和方法来开发自动化风险评分系统急性肺栓塞患者。该系统利用临床、实验室和成像数据的强大组合，将为多种 PE 结果测量提供患者特定的风险评分。此外，该项目将促进多中心合作，这将使我们有机会调查我们的方法的普遍性不同人群的 PE 患者，并训练、测试并最终部署我们的自动化预测模型各种临床环境。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation

MedShift：自动识别医学图像数据集管理的移位数据

DOI：
10.1109/jbhi.2023.3275104
发表时间：
2023
期刊：
IEEE Journal of Biomedical and Health Informatics
影响因子：
7.7
作者：
Guo, Xiaoyuan;Gichoya, Judy Wawira;Trivedi, Hari;Purkayastha, Saptarshi;Banerjee, Imon
通讯作者：
Banerjee, Imon

Developing medical imaging AI for emerging infectious diseases.

DOI：
10.1038/s41467-022-34234-4
发表时间：
2022-11-18
期刊：
Nature communications
影响因子：
16.6
作者：
通讯作者：

Efficient adversarial debiasing with concept activation vector - Medical image case-studies.

使用概念激活向量进行有效的对抗性去偏 - 医学图像案例研究。

DOI：
10.1016/j.jbi.2023.104548
发表时间：
2024
期刊：
Journal of biomedical informatics
影响因子：
4.5
作者：
Correa,Ramon;Pahwa,Khushbu;Patel,Bhavik;Vachon,CelineM;Gichoya,JudyW;Banerjee,Imon
通讯作者：
Banerjee,Imon

President Biden's Executive Order on Artificial Intelligence-Implications for Health Care Organizations.

拜登总统关于人工智能的行政命令——对医疗保健组织的影响。