对比机器学习模型对顺序结果的预测歧视:对急性腹患者脱水预测的案例研究
Kexin Qu1, Monique Gainey2, Samika S Kanekar3
1Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America.
PLOS digital health
|May 6, 2025
概括
在预测脱水严重程度时,像比例几率逻辑回归 (POLR) 这样的回归模型在外部测试数据上表现最好,在验证后优于机器学习方法. 外部验证对于准确的临床预测模型评估至关重要.
科学领域:
- 临床流行病学临床流行病学
- 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:
- 现有的对临床预测模型的统计回归和机器学习进行比较通常使用有缺陷的方法.
- 对于预测顺序分类结果的模型,缺乏适当的比较.
- 使用独立测试集进行外部验证对于评估现实世界模型性能至关重要.
研究的目的:
- 为了比较四种回归和机器学习方法的模型歧视,用于预测顺序脱水严重程度.
- 用内部验证 (bootstrap) 和外部前性测试集来评估模型性能.
- 确定最有效和高效的方法来构建临床预测模型的顺序结果.
主要方法:
- 他们比较了四种方法:比例赔率逻辑回归 (POLR),惩罚顺序回归 (RIDGE),分类树 (CART) 和随机森林 (RF).
- 模型预测了急性腹患者的脱水严重程度 (严重,有些,没有) 使用NIRUDAK研究数据.
- 模型歧视是使用顺序c指数 (ORC),通用c指数 (GC) 和平均二分式c指数 (ADC) 进行评估的.
主要成果:
- 随机森林 (RF) 在初始培训数据上显示出优异的歧视,但在内部引导验证后与其他方法可比.
- 所有模型在外部测试数据集上都表现出性能下降,RF和RIDGE的显著下降.
- 比例赔率逻辑回归 (POLR) 在外部测试集中表现最好,也是最节的.
结论:
- 对临床预测模型来说,前性的外部验证是必不可少的,因为内部验证可能会高估性能.
- 回归方法,当仔细构建时,可以与机器学习性能匹配顺序结果.
- 回归模型往往在临床上更易于解释,因此应促进它们的使用.
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