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临床表现 临床表现

Emma P Fischer1, Zahra Khodakarami1, Christopher A Brown1

  • 1University of Pennsylvania, Philadelphia, PA, USA.

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概括
此摘要是机器生成的。

一个新的机器学习模型预测了认知年龄,识别了对衰老有弹性或脆弱的个体. 从模型中删除种族,揭示了认知脆弱性的社会经济差异.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 老年学是一门学科.

背景情况:

  • 对神经退行性疾病的传统认知评估可能是可变和有偏见的.
  • 认知年龄模型通过预测认知分数和人口统计数据的年龄,提供了更强大的认知状态测量方法.
  • 这种方法有助于识别不同人群中对正常衰老的弹性或脆弱性.

研究的目的:

  • 开发和评估用于预测认知年龄的机器学习模型.
  • 调查人口学变量,包括种族和社会经济地位 (贫困地区指数 - ADI) 对认知衰老的影响.
  • 识别导致认知性和脆弱性的因素.

主要方法:

  • 一个随机的森林模型是使用来自11,752个认知无障碍个人的心理和人口统计数据 (NACC UDS 3) 构建的.
  • 该模型预测了"认知"年龄,并计算百分位数来表示脆弱性或弹性.
  • 该模型被评估,并没有自我认同的种族作为预测因素,百分位与贫困地区指数 (ADI) 相对相关.

主要成果:

  • 不包括种族的模型显示了认知年龄百分位数排名和ADI之间更强的关联,表明种族共变可能掩盖社会经济影响.
  • 排除种族增加了黑人参与者的百分位数,表明更高的脆弱性.
  • 预先的MRI分析表明,与脆弱个体相比,弹性个体的前带带膜和中带前皮层厚度更大.

结论:

  • 机器学习认知年龄模型有效地分析认知,并在大型数据集中识别异常的衰老模式.
  • 它成功地识别出表现出认知性或脆弱性的个体.
  • 了解人口统计学对模型预测的影响,可以更好地了解驱动弹性和脆弱衰老的因素.