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外部验证复杂性:在多种临床环境中对晚发性败血症预测模型的比较研究
IEEE transactions on bio-medical engineering
|October 6, 2025
概括
新生儿晚发性败血症 (LOS) 的预测模型在外部验证中显示精度下降. 这凸显了在各种新生儿重症监护室 (NICU) 设置中实施这些模型的挑战.
科学领域:
- 新生儿重症监护室新生儿重症监护室
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
背景情况:
- 新生儿晚发性败血症 (LOS) 对新生儿重症监护室 (NICU) 的早产婴儿构成重大威胁.
- 早期发现LOS对于改善婴儿后果至关重要.
- 现有的数据驱动的LOS预测模型由于有限的独立验证而面临普遍性挑战.
研究的目的:
- 为了评估两个不同的LOS预测模型的性能.
- 评估这些模型在不同环境中临床实施的可靠性.
- 了解外部验证对模型通用性的影响.
主要方法:
- 两个模型得到了验证:一个基于多通道特征的极端梯度增强模型 (MC-XGB) 和使用原始RR间隔 (RR-DNN) 的深度神经网络.
- 验证数据集包括内部 (荷兰),国家外部 (荷兰) 和国际外部 (美国) NICU 数据.
- 模型性能是通过在各种预测时间窗口的接收器操作特征曲线 (AUC) 下的面积来衡量的.
主要成果:
- 这两种模型在内部数据集上都达到0.82的峰值AUC.
- 对外部数据集的性能下降:RR-DNN AUC为0.80 (国家) 和0.69 (国际);MC-XGB AUC为0.72 (国家) 和0.60 (国际).
- 性能差异可能是由于临床实践,患者人口统计和监测技术的差异.
结论:
- 与内部数据相比,外部验证数据集中的模型性能显著下降.
- 在不同的NICU环境中实施LOS的预测模型带来了相当大的挑战.
- 标准化的指导方针和增强的数据共享对于开发更强大,更适用于临床的LOS预测模型至关重要.
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