在医疗保健中对机器学习分类器的后处理除偏移方法的实证比较.
Vien Ngoc Dang1, Víctor M Campello1, Jerónimo Hernández-González2
1Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
Journal of healthcare informatics research
|July 29, 2025
概括
后处理方法可以在不需要再培训的情况下减轻机器学习医疗保健模型中的偏差. 本研究评估了最先进的淘汰技术,揭示了公平性和准确性之间的权衡,以实现最佳实施.
科学领域:
- 机器学习 机器学习
- 医疗保健的不平等 医疗保健的不平等
- 算法公平性 算法公平性
背景情况:
- 医疗保健中的机器学习分类器可以使现有的健康差异延续或恶化,这是因为有偏见的培训数据.
- 解决这些偏见对于公平的医疗保健服务至关重要.
研究的目的:
- 严格比较医疗保健中机器学习模型的最先进的后处理脱方法.
- 评估预测性绩效与各种集团公平性指标之间的权衡.
主要方法:
- 对比多种后期加工的脱氧化技术.
- 跨多种合成和现实世界医疗保健数据集的评估.
- 使用各种绩效和公平性指标进行评估,包括对未经处理的属性的影响.
主要成果:
- 确定每个比较的退化方法的优点和弱点.
- 分析集团公平性与预测性绩效之间的权衡.
- 检查不同的集团公平性概念之间的权衡,以及对未处理属性的影响.
结论:
- 后处理方法提供了一种保护隐私的方式,以确保公平而不需要再培训.
- 医疗保健的最佳实施需要根据特定需求平衡准确性和公平性.
- 该研究提供了有效减轻医疗保健机器学习偏差的见解.
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