机器学习模型对临床死亡风险的预测差异:对COVID-19患者的多队列研究
Júlia Chaves Neuenschwander Magalhães1, Alexandre Dias Porto Chiavegatto Filho1
1Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, São Paulo, Brazil.
PloS one
|March 6, 2026
概括
预测COVID-19死亡率的机器学习 (ML) 模型显示了类似的整体性能,但不同的个人预测. 算法选择很重要,因为患者子组的表现各不相同,因此需要对上下文进行评估.
科学领域:
- 医疗保健中的机器学习
- 临床决策支持 临床决策支持
- 算法偏差是一种算法偏差.
背景情况:
- 机器学习 (ML) 算法在医疗保健中越来越多地用于临床决策.
- 算法多重性,即具有相似性能的模型产生不同的预测,可以引入偏差.
- 本研究研究了死亡率预测中的算法多重性及其对患者特征的影响.
研究的目的:
- 调查算法多重性对死亡率预测的影响.
- 评估患者特征如何影响机器学习模型决策.
- 评估机器学习模型在不同患者子组中的表现的一致性.
主要方法:
- 在巴西4,337名COVID-19患者的人口和实验室数据上训练了五种流行的ML模型.
- 评估模型性能,特征重要性和预测相似性.
- 使用k-means集群来识别患者子组,并评估这些集群内的模型性能变化.
主要成果:
- 所有模型的整体性能都很高 (平均R2=0.855),但个别预测的差异很大 (对式R2=0.56-0.80).
- 确定了五个不同的患者子组,在模型性能中存在显著差异 (F=73.18,p<0.001).
- 特定的算法 (TabPFN,LightGBM) 在"贫血"和"免疫缺陷"等子组中显示出不同的性能.
结论:
- 具有相似整体性能的机器学习模型可以在个人和子组层面产生不同的预测.
- 没有一个单一的算法在所有患者子组中始终超过其他算法.
- 对于异质的临床群体来说,对ML模型的上下文意识评估至关重要,而不仅仅依赖全球绩效指标.
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