TARGET-AI:在电子健康记录中针对性地部署人工智能心电图谱的基本方法
medRxiv : the preprint server for health sciences
|September 5, 2025
概括
通过将电子健康记录与心电图集成,提升人工智能对结构性心脏病 (SHD) 的查. 这种有针对性的方法可显著减少AI-ECG分析中的错误阳性,以更好地检测心脏病.
科学领域:
- 心脏病学
- 医学的人工智能
- 医学成像分析
背景情况:
- 应用于心电图 (ECG) 的人工智能显示出查结构性心脏病 (SHD) 的潜力.
- 临床整合受到高假阳性率和AI-ECG工具量身定制的部署策略的阻碍.
- 现有的方法缺乏整合患者的纵向数据以优化查.
研究的目的:
- 开发和评估TARGET-AI,一个多模式AI管道用于针对性AI-ECG查SHD.
- 整合纵向电子健康记录 (EHR) 数据与心电图像进行精确的查.
- 确定AI-ECG部署的医疗保健接触和患者表型的最佳交叉点.
主要方法:
- 开发了TARGET-AI,该管道使用了1亿8000万个EHR事件进行预训练的基础模型,用于患者嵌入和风险识别.
- 使用对比视觉语言模型训练754533对心电图-心声图来检测可调节性能的SHD.
- 在5,198名参与者中验证了联合策略,并在33,518名英国生物库参与者中进行了外部验证.
主要成果:
- AI-ECG基础模型区分了27种高AUROC的SHD亚型 (例如,左心室缩功能障碍的0.90).
- 与非目标选相比,TARGET-AI引导的选显著减少了错误的阳性预测 (中位数减少了87.8%) 和提高了F1得分 (中位数增加了0.25%).
- 在英国生物库进行的外部验证显示,假阳性结果减少了61. 7%,同时保持了敏感性.
结论:
- 通过整合EHR表型和多式心电图数据,TARGET-AI可实现AI-ECG查的上下文意识部署.
- 这种方法定义了一个可互操作的,基于数据的战略,用于在卫生系统中精确地部署人工智能查工具.
- 这些发现支持在诊断结构性心脏病时更有效,更准确地使用人工智能.
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