评估社会人口学因素对人工智能模型在预测痴呆症方面的影响:回顾性队列研究
Xingyi Liu1, Muskan Garg1, Maria Vassilaki2
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Harwick Building, 7th Fl., 205 3rd Ave SW, Rochester, MN, 55905, United States, 1 507-266-0376.
JMIR medical informatics
|February 17, 2026
概括
人工智能 (AI) 用于痴呆症预测的模型显示偏见对较低社会经济地位 (SES) 群体. 像HOUSES指数这样的个人级别的SES措施可能比区域级别的措施更有效地提高公平性.
科学领域:
- 医疗保健中的人工智能
- 生物医学信息学 生物医学信息学
- 健康 公平 卫生 公平
背景情况:
- 人工智能 (AI) 越来越多地用于医疗保健,引发了人们对公平性和与社会人口统计学因素 (如社会经济地位 (SES) 和性别) 相关的潜在偏见的担忧.
- 之前的研究表明,人工智能模型的性能可以受到SES和性别的影响,这可能会使历史上服务不足的人口处于不利地位.
研究的目的:
- 评估人工智能驱动的痴呆症预测模型中关于SES和生物性别的算法偏差.
- 为了比较个人层面 (HOUSES指数) 与区域层面 (区域贫困指数) 的SES措施在检测偏差方面的有效性.
- 评估名义和连续特征 (SMOTE-NC) 的合成少数群体过量采样技术对于偏差缓解的实用性.
主要方法:
- 利用了来自两个基于人口的队列的数据 (梅奥诊所关于衰老的研究和罗切斯特流行病学项目).
- 训练了四个人工智能模型 (随机森林,后勤回归,支持矢量机,天真贝叶斯) 使用电子健康记录数据来预测痴呆症发病.
- 在交叉的SES-sex子组中使用均衡错误率 (BER) 评估公平性和绩效,并应用SMOTE-NC来缓解偏差.
主要成果:
- 较低的SES群体通常表现出更高的BER,这证实了痴呆症预测模型中的算法偏差.
- 房屋指数 (HOUSES Index) 是个体级别的SES指标,与区域级别的指标相比,它显示出更有效的偏差检测和缓解的潜力.
- 偏差缓解技术虽然提高了一些组的公平性,但并没有普遍有利于所有子组,有时可能会降低模型的整体性能.
结论:
- 纳入社会人口学背景,特别是个人层面的SES指标,如HOUSES指数,对于在医疗保健中开发公平的AI至关重要.
- 选择SES衡量措施显著影响偏见评估和缓解策略.
- 未来的人工智能开发必须整合强大的偏见缓解策略,以防止加强健康差异.
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