开发一个公平和可解释的表现的时钟绘图测试,以减轻低教育和种族偏见
Jiaqing Zhang1,2,3, Sabyasachi Bandyopadhyay4, Faith Kimmet3,5
1Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA.
Scientific reports
|July 29, 2024
概括
时钟绘图测试的公平可解释表示 (FaIRClocks) 模型减轻了受教育程度有限的个体在认知查中的偏见. 这种人工智能工具确保了对认知功能的更公平的评估,解决了神经心理学评估中的关键差距.
科学领域:
- 神经心理学 神经心理学
- 人工智能的人工智能
- 健康 公平 卫生 公平
背景情况:
- 时钟绘图测试 (CDT) 是评估认知功能的常见工具.
- 现有的认知查方法可能会对教育不到8年的个人产生偏见.
- 这种偏见可能导致不准确的评估和医疗保健中的差异.
研究的目的:
- 开发一个公平和可解释的表现的时钟绘图测试 (FaIRClocks) 模型.
- 评估和减轻对受教育程度有限的个人进行认知查的分类偏见.
- 提高神经心理学评估的准确性和公平性.
主要方法:
- 利用了来自国家健康和衰老趋势研究 (NHATS) 的 10 维深度学习功能.
- 精心调整的嵌入式与手术前认知查程序的时钟图纸.
- 预测的迷你心理状态检查 (MMSE) 总分,注意力 (ATT-C) 和记忆力 (MEM-C) 复合分.
- 使用公平度指标重新权衡雇员样本,以平衡人口群体之间的表现.
主要成果:
- 最初的分类器显示,教育程度低的人群的I型错误率为100%,将教育与认知障碍混为一谈.
- 使用公平度指标进行重权衡,成功地平衡了教育和种族群体之间的敏感性,特异性和预测值.
- FaIRClocks模型在认知查中证明了提高公平性.
结论:
- FaIRClocks模型提供了一种有希望的方法来识别和减少认知查中的偏见.
- 这种人工智能驱动的工具可以增强对不同人群的公平的手术前认知评估.
- 解决教育差异对于准确和公平的神经心理评估至关重要.
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