从成人和儿科心电图进行深度生存分析:一个多中心基准研究
Platon Lukyanenko1,2, Joshua Mayourian2,3, Mingxuan Liu4
1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
BioData mining
|December 18, 2025
概括
用于心电图 (AI-ECG) 的人工智能显示出对死亡率预测的前景. 多站点培训和深度生存模型,包括人口统计数据,提高AI-ECG在不同患者群体的概括性和性能.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 应用于心电图 (AI-ECG) 的人工智能显示出死亡率预测的潜力.
- 异质的人工智能方法和私有数据集限制了对有效的人工智能方法的概括性见解.
- 需要对多样化的医疗中心队列进行系统评估,以了解AI-ECG模型设计选择.
研究的目的:
- 系统地评估AI-ECG模型设计选择,以预测死亡率.
- 为了比较不同的AI方法,包括深度生存和基于地平线的分类方法.
- 评估人口共变量和多站点培训对AI-ECG模型性能的影响.
主要方法:
- 通过使用深度生存和基于地平线的分类方法,评估了所有原因死亡率预测的模型.
- 将各种神经架构 (CNN,变压器) 与仅人口统计和梯度增强基线进行比较.
- 利用了三个大型,多元化的医疗中心队列:MIMIC-IV (美国),Code-15 (巴西) 和BCH (美国).
主要成果:
- 顶级AI-ECG模型实现了良好的预测性能 (中位对应度为0.78-0.83).
- 将年龄和性别纳入所有数据集的表现得到了改善.
- 多站点培训显著优于特定站点培训 (5-22%的改善),即使是在成人和儿科队列中.
结论:
- 深度生存方法为AI-ECG死亡率预测提供了基于地平线的分类器的优势.
- 年龄和性别等人口统计特征提高了预测准确度.
- 跨队列培训对于开发适用于不同人群的强大和可泛化AI-ECG模型至关重要.
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