对儿童尾炎诊断,管理和严重程度的预测模型的外部验证
Ričards Marcinkevičs1, Kacper Sokol1, Akhil Paulraj1
1Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Frontiers in pediatrics
|September 15, 2025
概括
儿童尾炎的机器学习模型在医院之间转移时显示性能下降. 重新训练模型提高了性能,但没有完全恢复它,突出了现实世界的临床应用中的挑战.
科学领域:
- 医疗信息学 医疗信息学
- 儿科手术 儿科手术
- 医疗保健中的人工智能
背景情况:
- 尾炎是儿童和青少年经常出现的疾病.
- 机器学习 (ML) 模型可以帮助诊断,评估严重程度和指导儿童尾炎的管理.
- 模型的可靠性,安全性和稳定性对于在各种临床环境中采用至关重要.
研究的目的:
- 为了外部验证儿童尾炎诊断,管理和严重程度预测的ML模型.
- 评估不同医院实践和患者群体对模型性能的影响.
- 评估模型再培训在改善新环境中的性能方面的有效性.
主要方法:
- 在德国队列 (430名患者) 上训练的ML模型的外部验证,使用独立的德国队列 (301名患者).
- 包括人口,临床,评分,实验室和超声波参数.
- 探索模式再培训的好处和变量重要性分析.
主要成果:
- 数据集之间参数分布的显著差异导致诊断,管理和严重程度的预测性能下降.
- 用外部数据重新训练模型提高了性能,但仍然低于原始研究的性能.
- 关键预测变量在两个医院数据集中都显示出一致性.
结论:
- 对于儿科尾炎的转移ML模型实现了令人满意的性能,但与原始数据性能相比,其性能不足于最佳.
- 由于临床实践,人口统计和外部因素的差异,医院之间转移模型存在挑战.
- 模型再培训提供了部分改善,但无法完全恢复转移期间丢失的性能,这表明其补救能力有限.
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