一种可解释的基于人工智能的方法来测量认知储备
Sifat Redwan Wahid1, Sabir Saheel1, Jack Quigley1
1Department of Computer Science, University of Minnesota Duluth.
Studies in health technology and informatics
|May 13, 2025
概括
量化认知储备 (CR) 是一个挑战. 机器学习对CR测量具有前景,但临床使用需要透明度和标准化变量.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 认知储备 (Cognitive Reserve,简称CR) 解释了个体在对大脑病理的认知性方面的差异.
- 了解CR对于解决认知衰退至关重要.
- 目前用于量化CR的方法缺乏有效性和广泛接受.
研究的目的:
- 系统地审查目前用于定量认知储备的方法.
- 确定机器学习 (ML) 在开发可靠的CR量化方法方面的潜力.
- 在临床环境中解决ML模型的局限性,特别是它们的"黑子"性质.
主要方法:
- 对量化CR的方法进行了系统的文献审查.
- 随后的文献审查集中在基于ML的CR测量方法上.
- 21篇相关论文被纳入了最终的系统审查.
主要成果:
- 机器学习 (ML) 方法显示出数据驱动和可访问的CR量化的巨大潜力.
- 缺乏标准化的代理变量和模型透明度阻碍了基于ML的CR测量的临床采用.
- 审查的方法旨在提高CR测量的透明度和可解释性.
结论:
- 机器学习为推进CR量化提供了一个有希望的途径.
- 变量的标准化和增强的模型解释性对于临床整合ML在CR评估中至关重要.
- 未来的研究应该专注于开发透明和标准化的ML模型,以可靠地测量CR.
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