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相关概念视频

Language and Cognition01:27

Language and Cognition

343
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
343
Cognitive Development During Adulthood01:30

Cognitive Development During Adulthood

94
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,...
94

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相关实验视频

Updated: Jun 26, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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机器学习用于使用听觉和人口因素预测认知缺陷.

Christopher E Niemczak1,2, Basile Montagnese1, Joshua Levy3,4,5,6

  • 1Geisel School of Medicine at Dartmouth, Space Medicine Innovations Laboratory, Lebanon, NH, United States of America.

PloS one
|May 14, 2024
PubMed
概括

审计评估可以预测艾滋病毒感染者的神经认知缺陷 (PLWH). 使用听觉数据的机器学习模型显著提高了预测准确度,与仅仅人口因素相比,为早期干预提供了潜力.

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科学领域:

  • 神经科学是一个神经科学.
  • 传染性疾病 传染性疾病
  • 机器学习 机器学习

背景情况:

  • 认知功能障碍是艾滋病毒感染者 (PLWH) 的一个问题,特别是在资源较低的环境中.
  • 早期发现认知障碍对于及时干预至关重要.
  • 对神经认知缺陷的听觉评估的预测价值,除了人口统计数据,还需要进一步调查.

研究的目的:

  • 利用机器学习来预测神经认知缺陷.
  • 评估听力测试和人口因素在预测认知障碍方面的有效性.

主要方法:

  • 在坦桑尼亚达累斯萨拉姆进行了一项纵向研究,涉及478名参与者 (349名PLWH,129名HIV阴性).
  • 使用听觉变量和人口统计数据训练和测试机器学习算法.
  • 神经认知功能障碍被定义为在Kiswahili蒙特利尔认知评估中得分<26.

主要成果:

  • 结合听觉变量的机器学习模型证明了神经认知缺陷的优异预测.
  • 斯和内核原始贝叶斯分类器显示出高预测性能,AUC值分别为0.91和0.87.
  • 没有听觉特征的模型表现明显差 (p < .001),突出了听觉评估的增量预测价值.

结论:

  • 听觉变量显著提高认知功能的预测.
  • 易于管理的听觉测试可以作为神经认知表现的客观预测指标,适合全球应用.
  • 进一步研究用于预测认知结果的机器学习算法是有必要的.