转移学习用于COVID-19预测建模:对12家医院的多中心研究
Carine Savalli1, André Henrique Alves Carneiro2, Fabiano Barcellos Filho3
1Department of Public Politics and Public Health, Federal University of São Paulo, Santos, Brazil.
Annals of epidemiology
|June 1, 2025
概括
转移学习改善了医院中COVID-19患者的重症监护室 (ICU) 入院预测. 这种方法利用了高性能站点的知识来增强低性能或数据有限的医院的模型.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 在COVID-19研究研究中.
背景情况:
- 预测重症监护室 (ICU) 入院对于管理COVID-19患者至关重要.
- 多中心研究由于患者群体和数据的变化而面临挑战.
研究的目的:
- 在多家医院应用转移学习来预测COVID-19患者的ICU入院情况.
- 评估转移学习在改善模型概括和性能方面的有效性.
主要方法:
- XGBoost算法使用来自12家医院的人口和实验室数据进行训练.
- 确定了一种表现最好的医院模型,并进行了外部验证.
- 转移学习是通过微调最佳模型与其他医院的数据来实现的.
主要成果:
- 观察到局部预测性能的显著变化 (AUC 0.6239-0.9410).
- 外部验证显示,在11家医院中的6家,AUC低于0.7.
- 与外部验证相比,在纳入20棵新树后,转移学习在9家医院改善了AUC.
结论:
- 转移学习有效地利用高绩效医院的知识.
- 该方法加速了模型培训,并提高了在数据有限或性能较低的医院的适应性.
- 这种方法可以提高预测模型在多中心环境中的效率和通用性.
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