在测试时间无监督检测和纠正模型校准转移
概括
一种新的方法 (CaDC) 使用未标记的数据检测和纠正模型校准转移. 这提高了临床预测模型的通用性和性能,比如用于毒症预测的模型,在不同的医院.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的机器学习
- 预测建模预测建模
背景情况:
- 临床预测模型需要通用性和随着时间的推移一致的性能.
- 由于数据分布或流行率的变化,模型校准转移阻碍了模型的概括性.
- 在多样化的医疗保健系统中保持模型性能对于广泛采用至关重要.
研究的目的:
- 提出一种用于检测和纠正模型校准转移的新方法.
- 开发一种灵活的方法 (CaDC),可用于任何深度学习临床模型.
- 为了验证该方法在改善败血症预测模型方面的有效性.
主要方法:
- 开发了一个模型校准检测和校正 (CaDC) 方法.
- 利用目标医院的未标记数据进行校准调整.
- 将CaDC应用于使用三个大型美国患者队列 (545,089名患者) 的败血症预测模型.
主要成果:
- 该CaDC方法成功地检测并纠正了毒症预测模型中的校准转移.
- 使用CaDC的模型更有效地实现了预定义的正预测值 (PPV).
- 对于20%的目标PPV,CaDC在外部验证队列中的性能从18.0%提高到23.1%.
结论:
- 该 CaDC 方法提高了临床预测模型的概括性和可靠性.
- 这种方法可以维持在多个医院系统中部署的模型的性能声明.
- 在现实临床环境中,CaDC为解决校准偏移的实际解决方案提供了实用解决方案.
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