减轻对少数群体人工智能死亡率预测的偏见:一种转移学习方法
Tianshu Gu1,2, Wensen Pan3, Jing Yu3
1Department of Clinical Pharmacy and Translational Science, The University of Tennessee Health Science Center, Memphis, TN, USA.
BMC medical informatics and decision making
|January 17, 2025
概括
用于预测COVID-19死亡率的人工智能 (AI) 模型显示了人口偏见. 转移学习显著改善了跨种族和族群的公平性和预测性表现,突出了它在减轻医疗保健差异方面的潜力.
科学领域:
- 医疗保健 人工智能 医疗保健 人工智能
- 健康差距 研究 研究 研究 研究
- 机器学习在医学中的应用
背景情况:
- COVID-19大流行强调了人工智能在死亡率预测和医疗保健决策中的作用.
- 现有的人工智能模型风险是由于人口偏见,特别是对少数群体而言,会使健康差异持续存在.
研究的目的:
- 调查人工智能模型中的人口偏见,以预测COVID-19死亡率.
- 评估转移学习在提高AI模型在不同人群中的公平性方面的有效性.
主要方法:
- 使用CDC COVID-19数据 (2020-2024) 的回顾性队列研究.
- 分析了跨种族/族群的AI模型性能.
- 应用转移学习,以适应预先训练的模型,以提高人口公平性.
主要成果:
- 决策树和随机森林模型显示,黑人,西班牙裔/拉丁裔和亚洲人群的准确性和精度有所提高.
- 转移学习导致了显著的精度增长,例如,西班牙裔/拉丁裔DT模型的精度从0.3805增加到0.5265.
- 梯度提升机产生了混合的结果;物流回归显示了最小的变化. 一些群体,比如美洲印第安人,表现下降.
结论:
- 人工智能有可能预测COVID-19死亡率,但需要解决人口偏见.
- 转移学习有效地减轻了偏见,并促进了AI医疗保健模型中的公平性.
- 未来的人工智能开发必须优先考虑所有人口群体的公平表现.
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