在小儿尿路感染模型中测量和减少种族偏见
medRxiv : the preprint server for health sciences
|October 4, 2023
概括
重新规范临床预测模型可以减少种族偏见,但可能会降低整体性能. 同时实现所有指标的公平性并不总是可能的,强调了医疗保健AI的权衡.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 算法公平性 算法公平性
背景情况:
- 使用种族的临床预测模型可以加剧医疗保健差异.
- 模型的重新规范提供了一个潜在的策略,以减轻种族偏见.
- 旨在减少儿童尿路感染的UTICalc模型被检查是否存在偏差.
结论:
- 排除种族和添加UTICalc的功能显示了对种族偏见和表现的影响.
- 该研究证实,在所有指标上实现完美的公平性往往是无法实现的.
- 在为公平性修改临床模型时,仔细考虑权衡至关重要.
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