评估和改进GFR估计算法的性能和种族公平性
Linying Zhang1,2, Lauren R Richter1, Tevin Kim1
1Department of Biomedical Informatics Columbia University, New York, NY, USA.
medRxiv : the preprint server for health sciences
|January 23, 2024
概括
这项关于功能预测算法的研究发现,复杂模型可以提高准确性,但不能减少种族差异. 公平的医疗保健算法需要更客观的患者数据.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
- 临床预测模型临床预测模型
背景情况:
- 数据驱动的算法在临床环境中越来越多地使用.
- 确保公平性和理解绩效影响对于公平的医疗保健至关重要.
- 电子健康记录 (EHR) 为预测建模提供了丰富的数据.
研究的目的:
- 调查建模选择如何影响估计淋巴膜过率 (GFR) 算法的性能和公平性.
- 为了比较不同的方法 (CKD-EPI,流行病学,基于EHR) 和机器学习模型用于GFR估计.
- 评估跨种族群体的算法公平性.
主要方法:
- 使用EHR数据开发了GFR预测算法.
- 比较CKD-EPI方程,流行病学模型和基于EHR的模型.
- 使用RMSE,中位差和P30指标评估了四种机器学习模型 (线性回归,SVM,随机森林,神经网络).
- 通过分析非裔美国人和非非裔美国患者之间的差异性表现来评估公平性.
主要成果:
- 更复杂的模型,如随机森林回归,并包括临床特征减少了GFR估计误差.
- 变量"赛车"对整体误差,精度和差异性能的影响微不足道.
- 尽管有所改善,但非洲裔美国患者的估计误差仍然更高,这表明持续存在的公平性问题.
- 算法公平性没有得到改善,突出了对更客观的患者特征的需求.
结论:
- 虽然复杂的机器学习模型提高了GFR估计的准确性,但它们并不能本质上解决种族绩效差异.
- 将种族作为一个变量没有显著影响模型的性能或公平性.
- 未来的研究必须确定并纳入更客观的患者数据,以提高算法公平性并实现公平的医疗保健结果.
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