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机器学习中的算法公平和自闭症的预测使用电子健康记录

Amber M Angell¹, Yongqiu Li², Jiang Bian²

¹University of Southern California, Los Angeles, CA, USA.

Studies in health technology and informatics

|August 8, 2025

概括

使用电子健康记录 (EHR) 进行自闭症谱系障碍 (ASD) 诊断的机器学习模型显示出重大公平性问题. 这些模型在性别上表现不公平,突出了自闭症识别中的偏见.

科学领域:

医疗信息学医疗信息学
计算精神病学是一种计算精神病学.
医疗保健中的机器学习

背景情况:

早期诊断自闭症谱系障碍 (ASD) 对干预至关重要.
应用于电子健康记录 (EHR) 的机器学习 (ML) 对自闭症识别有希望.
现有的ASD诊断工具表现出基于性别的差异,需要公平的ML模型.

研究的目的:

使用EHR数据开发基于ML的ASD诊断预测模型.
评估这些ML模型跨性别的算法公平性.
识别和量化男孩和女孩之间的自闭症预测中的潜在偏差.

主要方法:

追溯病例控制研究设计.
从EHR中利用了大量的ASD和无ASD儿童队伍 (70,803例ASD病例,212,409例对照).
开发了后勤回归和XGBoost模型,通过准确性,回忆,精度,F1得分和AUC等指标来评估性能;使用机会平等和均赔率来评估公平性.

主要成果:

使用EHR数据进行ASD预测的ML模型展示了重要的公平性问题.
在男孩和女孩之间观察到绩效差异,表明潜在的偏见.
标准性能指标没有完全捕捉算法偏差的程度.

结论:

目前使用EHR诊断ASD的ML模型对性别不公平.
算法公平性评估对于开发可靠的ASD预测工具至关重要.
需要进一步的研究来缓解偏见,并确保所有儿童均可识别ASD.

关键词:

自闭症自闭症自闭症电子健康记录是电子健康记录.预测建模预测建模

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