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了解用于校正类失衡的随机重新抽样技术及其对临床风险预测模型校准和歧视的影响
Marco Piccininni1, Maximilian Wechsung2, Ben Van Calster3
1Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany; Digital Engineering Faculty, University of Potsdam, Potsdam, Germany; Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Journal of biomedical informatics
|June 7, 2024
概括
类失衡校正方法,如随机低采样或过采样,可能会损害临床预测模型的校准. 一个拟议的插件估计器在不牺牲歧视性能的情况下改进了校准,提供了更有效的方法.
科学领域:
- 临床预测建模临床预测建模
- 生物统计学 生物统计学
- 机器学习在医疗保健中的应用
背景情况:
- 阶级不平衡是开发临床预测模型的常见挑战.
- 经常采用诸如随机低样本和过样本等校正策略.
- 这些策略对模型校准和歧视的影响尚未完全理解.
研究的目的:
- 调查阶级失衡纠正策略对临床预测模型内部有效性的后果.
- 评估对校准和区别性能的影响.
- 提出和评估来自不平衡数据集的预测的校正方法.
主要方法:
- 利用启发式直觉和正式的数学推理来分析概率关系.
- 开发了一个插件估计器,用于从人工平衡数据集进行预测.
- 进行蒙特卡洛模拟并分析现实数据 (国际中风试验数据库).
- 评估后勤回归和基于树的校准和歧视模型 (ROC曲线下的面积 - AUC).
主要成果:
- 模拟和现实数据显示,在没有纠正的不平衡数据 ("天真"模型) 上训练的模型中,校准不佳.
- 拟议的插件估计器与标准类不平衡校正技术相比,总体上提高了校准.
- 插件估计器实现了与使用类不平衡校正的模型相似的歧视性能 (AUC).
结论:
- 对于类不平衡的随机重新抽样技术并不能始终改善歧视 (AUC),并且很难为校准预测提供理由.
- 错误地应用类失衡纠正可能导致数据使用不足于最佳,风险预测模型不太有效.
- 插件估计器为在不平衡的临床数据集中进行校准预测提供了更可靠的方法.
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