对部署的临床预测模型进行持续评估的监测策略
Grace Y E Kim1, Conor K Corbin2, François Grolleau1
1Center for Biomedical Informatics Research, Stanford, CA, USA.
Journal of biomedical informatics
|June 7, 2025
概括
新的监测策略,依从权重和采样权重,准确评估临床机器学习模型的性能,并改善再培训,即使数据漂移和反循环.
科学领域:
- 机器学习在医疗保健中的应用
- 临床决策支持系统临床决策支持系统
- 数据漂移和模型监控的数据漂移.
背景情况:
- 临床实践中的机器学习分类器需要持续监测和再培训,以应对来自不断变化的实践和患者群体的数据漂移.
- 部署诱导的反循环,模型预测改变结果,如果不考虑,可能导致不准确的性能估计和退化的再培训.
研究的目的:
- 模拟反循环对临床机器学习分类器监控的影响.
- 提出和评估反意识的监控策略,作为解决部署诱导的反循环的解决方案.
- 评估这些新策略在准确评估部署后模型性能和实现安全再培训方面的表现.
主要方法:
- 拟议的依从权重和采样权重监测作为两个反循环意识的监测策略.
- 利用模拟来评估这些策略在评估部署后模型性能中的忠实性.
- 评估了这些策略能够启动安全和准确的分类器再培训的能力.
主要成果:
- 与反循环存在的标准方法相比,依从权重和采样权重的策略对基准真实分类器性能具有更高的忠实性.
- 标准监测方法产生了不准确的绩效估计.
- 在数据漂移的模拟中,使用标准方法进行再培训导致AUROC显著下降 (0.52从0.72),而反意识的策略恢复了性能至0.67.
结论:
- 附加权重和采样权重策略提供了更准确的分类器性能估计,与没有治疗的潜在结果保持一致.
- 基于这些反意识策略的再培训显示,与标准方法相比,对数据漂移和反循环的性能恢复优越.
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