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临床表现 临床表现

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概括
此摘要是机器生成的。

常见的认知测试误诊少数群体. 人工智能工具CognoSpeak在不同人群中显示出有希望的结果,但语言差异会影响结果. 为了公平的认知评估,需要进一步的研究.

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 语言学的语言学.

背景情况:

  • 当前的认知评估工具表现出偏见,经常误诊来自少数民族和英语作为附加语言的人的个体.
  • 由人工智能驱动的CognoSpeak旨在提供基于语言的记忆评估,但需要多样化的训练数据来减轻固有的偏见.
  • 传统的参与研究障碍阻碍了纳入认知评估研究中多样化的群体.

研究的目的:

  • 评估传统认知评估和基于AI的CognoSpeak工具在不同种族和语言群体中的诊断准确性.
  • 调查语言背景对认知评估表现的影响,特别是对于非母语英语的人.
  • 评估以社区为中心的研究方法在招募各种参与者进行认知评估研究方面的有效性.

主要方法:

  • 社区研究冠军受训招募和管理笔纸 (RUDAS,MCE,MoCA) 和自动化 (CognoSpeak) 认知评估.
  • 招募了来自南亚,索马里和中国社区团体的146名参与者,占CognoSpeak培训队伍中BAME参与者的60%以上.
  • 对参与者进行认知评估,所有参与者都被证实是认知完好无损的.

主要成果:

  • 蒙特利尔认知评估 (MoCA) 错误地将39.8%的参与者列为认知障碍者,而罗兰普世痴呆症评估量表 (RUDAS) 和多元文化认知检查 (MCE) 错误地分类了只有0.8% (p<.001).
  • 索马里参与者在CognoSpeak的口语流性任务中得分明显低于单语英语使用者 (p<.001).
  • 索马里语中缺少音符"P"可能会人为地降低流利度,这凸显了认知评估中语言考虑的重要性.

结论:

  • 由于潜在的偏见,MoCA可能不适合少数群体和非母语英语的人.
  • 一种以社区为基础的协作研究方法有效地减少了不同种族群体参与的障碍.
  • 进一步的分析将展示CognoSpeak在各种语言背景和种族的准确性,旨在实现更公平的认知评估.