将公平意识纳入临床语言处理模型
Rawan Abulibdeh1, Yihang Lin1, Sepehr Ahmadi1,2
1Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.
Communications medicine
|February 23, 2026
概括
一个层次的卷积神经网络在从临床文本中预测种族方面超过了变压器模型,实现了更高的准确性和公平性. 量身定制的公平干预措施至关重要,因为依赖于模型的结果突显了电子健康记录中的系统偏见.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 临床AI的公平部署需要在不同的人群中保持一致的表现.
- 在EHR中缺少/不一致的种族数据阻碍了队列代表性和偏见评估.
- 这项研究评估了AI模型的性能和从临床文本中预测种族的公平性.
研究的目的:
- 从临床文本中比较用于种族预测的深度学习模型.
- 评估公平意识优化对模式公平性的影响.
- 识别导致算法偏差的架构和系统因素.
主要方法:
- 通过使用双相主动学习框架,比较了四个变压器模型和一个层次CNN.
- 应用了公平意识损失函数来缓解种族差异.
- 通过十倍交叉验证和子集团审计评估业绩和股权.
主要成果:
- 层次CNN实现了比变压器更高的准确性和公平性 (宏 F1 = 98.4%).
- 公平性约束改善了变压器的平价性,但降低了层次模型性能.
- 持续存在的差异表明了架构限制和系统偏见.
结论:
- 在临床NLP模型中整合公平性是可行的,但取决于模型.
- 与临床文本结构一致的架构本质上促进了公平性.
- 上游文档不平等导致算法偏见,需要量身定制的干预.
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