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Cognitive Development During Adulthood01:30

Cognitive Development During Adulthood

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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使用多模态传感器数据和机器学习方法识别和预测认知衰退

Aparna Joshi1, Jun Ha Chang2, Guillermo Basulto-Elias1

  • 1Iowa State University.

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|June 30, 2025
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概括
此摘要是机器生成的。

自然主义的驾驶行为和睡眠模式显示出作为检测阿尔茨海默氏症认知衰退的非侵入性数字生物标志物具有前途.

关键词:
这是阿尔茨海默氏症.数字生物标志物数字生物标志物离开一个主体的方法.机器学习 机器学习轻度认知障碍 轻度认知障碍自然主义的驾驶方式

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科学领域:

  • 神经科学是一个神经科学.
  • 老年学是一门学科.
  • 数字健康数字健康

背景情况:

  • 阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 给全球健康带来了重大挑战,需要早期检测方法.
  • 目前对AD和MCI的诊断工具往往是侵入性的和昂贵的,推动了可扩展的,非侵入性的生物标志物的需求.
  • 自然主义的驾驶行为和睡眠模式越来越多地被认为是数字生物标志物的潜力.

研究的目的:

  • 研究自然驾驶行为和睡眠数据作为识别认知衰退的数字生物标志物的有效性.
  • 开发和验证一种多模式框架,用于对患AD和MCI风险的个体进行认知状态的分类和预测.
  • 评估这种方法在神经退行性疾病的早期检测和监测方面的潜力.

主要方法:

  • 研究了一组118名参与者 (8个AD,65个MCI,45个认知健康) 的队列.
  • 收集了多模式数据,包括人口统计,认知评估,3个月的自然驾驶数据和睡眠模式 (动图).
  • 实施了基于XGBoost的框架,使用Leave-One-Subject-Out交叉验证 (LOSO-CV) 进行两阶段验证 (分类和1年预测).

主要成果:

  • 多模分类器实现了强大的分类性能 (精度68.64%,精度73.97%,F1得分74.48%) 和预测性能 (精度70.48%,精度71.88%,F1得分74.80%).
  • 纳入人口统计和驾驶特征的模型显示了最高的回忆率 (76.39%) 进行分类.
  • 关键的预测特征包括性别,平均觉醒时间,年龄,平均加速和睡眠效率.

结论:

  • 自然驾驶行为和睡眠特征作为有价值的,非侵入性的数字生物标志物用于认知评估.
  • 开发的多模式框架在分类和预测认知衰退方面表现出强大的能力.
  • 这种方法为早期发现和监测神经退行性疾病和其他慢性疾病提供了可扩展和通用的方法.