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FaIRClocks:时钟绘图测试的公平和可解释的表现,以减轻对较低教育群体的分类器偏见
Jiaqing Zhang1,2, Sabyasachi Bandyopadhyay3,2, Faith Kimmet4,5
1Department of Electrical and Computer Engineering, University of Florida.
Research square
|October 27, 2023
概括
时钟图纸的公平可解释表示 (FaIRClocks) 模型使用时钟图纸来评估认知功能. 它通过减轻偏见,确保对受教育程度较低的人进行准确的预测.
科学领域:
- 神经心理学 神经心理学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 时钟绘图测试 (CDT) 是评估认知能力的常见工具.
- 现有的认知评估方法可能会对具有较低教育背景的个人产生偏见.
- 需要公平和可解释的工具来评估不同人群的认知状态.
研究的目的:
- 开发一个公平和可解释的表现时钟图纸 (FaIRClocks) 模型.
- 评估和减轻低学历的个人认知状态预测中的偏见.
- 使用时钟图纸准确预测认知得分 (MMSE,注意力,记忆).
主要方法:
- 利用相关性因素变化自编码器 (RF-VAE) 来从时钟图纸中创建 10 维的隐藏嵌入.
- 在国家健康和衰老趋势研究 (NHATS) 数据集上预先训练了RF-VAE.
- 精心调整的嵌入式用于预测迷你心理状态考试 (MMSE) 和复合注意力 (ATT-C) 和记忆 (MEM-C) 评分.
- 使用公平度指标重新权衡雇员样本,以平衡教育各级的分类器表现.
主要成果:
- 最初的分类器显示了100%的I型错误率,将低学历与认知障碍混为一谈.
- 在重新加权后,FaIRClocks模型在低教育和高等教育的人群中显示出相同的性能.
- 该模型成功地从时钟图纸中识别了注意力和记忆缺陷.
结论:
- 该FaIRClocks模型提供了一个公平和可解释的方法,用于使用时钟图的认知评估.
- 这种方法减轻了预测认知状态的教育偏见.
- FaIRClocks可以准确地检测注意力和记忆障碍,在不同教育背景下表现同样好.
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