展示MIDRC和N3C之间的互操作性:一个COVID-19严重性预测使用案例
Heather M Whitney1,2, Rachel Baccile3, Hui Li4,5
1Department of Radiology, University of Chicago, Chicago, IL, USA. hwhitney@uchicago.edu.
Journal of imaging informatics in medicine
|August 15, 2025
概括
将MIDRC的医学成像数据与N3C的临床记录集成,改善了COVID-19严重程度的预测. 结合成像和临床数据,在严重的结果中实现了更高的预测准确性.
科学领域:
- 医疗信息学医学信息学
- 数据科学是数据科学.
- 放射学 放射学是一门学科.
背景情况:
- 科学数据的互操作性对于多模式研究至关重要,与FAIR数据原则保持一致.
- 医学影像和数据资源中心 (MIDRC) 和国家COVID队列协作 (N3C) 是COVID-19研究的关键资源.
研究的目的:
- 评估MIDRC成像数据和N3C临床数据之间的互操作性潜力.
- 评估成像和临床数据的综合实用性,以预测COVID-19严重程度.
主要方法:
- 使用隐私保护记录链接,MIDRC和N3C之间的患者级数据匹配.
- 在N3C中识别COVID-19阳性病例和胸部X射线手术,间隔与MIDRC图像相匹配.
- 利用查尔森并发症指数 (CCI) 和最低平均动脉压 (MAP) 作为严重COVID-19预测的临床指标.
主要成果:
- 使用成像特征和临床指标 (CCI > 2,MAP < 70) 的综合模型实现了0.73的曲线下的面积 (AUC),用于预测严重的COVID-19.
- 仅使用成像数据 (AUC 0.67) 或临床数据 (AUC 0.69) 的模型显示预测性能较低.
- 在两个存储库之间成功匹配了117名患者和139张独特的图像.
结论:
- 在成像和临床资源之间跨平台的数据共享增强了多模式研究的潜力.
- 整合多样化的数据来源显著改善了对严重COVID-19结果的预测.
- 这种方法支持更广泛的合作研究,并推进科学数据管理.
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