一种以数据为中心的方法来检测和减轻儿科心理健康中的人口偏见
Julia Ive1, Paulina Bondaronek2, Vishal Yadav3
1University College London, Institute of Health Informatics, London, UK. j.ive@ucl.ac.uk.
Communications medicine
|March 5, 2026
概括
这项研究开发了一个人工智能去偏见框架,以减少儿科心理健康查中的性别差异. 人工智能模型通过解决临床笔记中发现的偏见来提高诊断公平性.
科学领域:
- 医疗保健中的人工智能
- 计算语言学 计算语言学
- 儿科心理健康 儿科心理健康
背景情况:
- 医疗保健人工智能可以从训练数据中延续偏见,加剧健康差异.
- 人工智能中的偏见缓解主要集中在结构化数据上,忽视了心理健康中的非结构化临床笔记.
- 临床笔记中的语言变异和数据稀疏性为AI偏差检测和减少带来了独特的挑战.
研究的目的:
- 在用于儿科心理健康查的AI模型中检测和减少非生物文本偏差.
- 解决青少年心理健康中人工智能驱动的诊断准确性的基于性别的差异.
- 开发一个以数据为中心的框架,以消除临床文本数据的偏见.
主要方法:
- 从电子健康记录中分析了约2万例儿科焦虑病例和对照 (5-15岁).
- 微调的基于变压器的AI模型用于焦虑预测,评估跨性别子组的分类均等性.
- 使用信息化术语过和系统的性别偏见文本替换来缓解偏见,并通过临床相关性与LIME进行验证.
主要成果:
- 在女性青少年中识别出系统性的低诊断 (准确性降低了4%,错误阴性率高了9%).
- 观察到临床笔记长度和性别词语分布的显著差异.
- 消除偏差框架将诊断偏差降低了高达27%,提高了模型性能公平性.
结论:
- 开发和评估一个以数据为中心的去偏见框架,以解决临床文本中的性别差异.
- 该框架中和了偏见的语言,并使信息密度正常化,同时保持临床相关性.
- 在临床部署除偏差框架之前,需要进一步验证.
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