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相关概念视频

Language and Cognition01:27

Language and Cognition

342
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.
342
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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相关实验视频

Updated: Jun 25, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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基于机器学习的认知衰退进展预测模型:在多语言环境中使用语音分析进行比较研究.

B Ceyhan1, S Bek2, T Önal-Süzek1

  • 1Department of Bioinformatics, Graduate School of Natural and Applied Sciences, Mugla Sitki Kocman University, Mugla 48000, Türkiye.

JAR life
|May 22, 2024
PubMed
概括

在轻度认知障碍 (MCI) 中早期检测痴呆症进展是可能的,使用语音分析. 来自自发言的音频特征显示了跟踪认知衰退的跨语言潜力,提供了一个具有成本效益的解决方案.

关键词:
自发的演讲自发的演讲痴呆症 痴呆症是一种痴呆症.机器学习是机器学习.心理状态和痴呆症测试的精神状态和痴呆症测试.轻度的认知障碍 轻度的认知障碍

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科学领域:

  • 神经学 神经学
  • 计算语言学 计算语言学
  • 机器学习 机器学习

背景情况:

  • 轻度认知障碍 (MCI) 是痴呆症的前体,需要早期检测才能进行干预.
  • 痴呆症影响认知功能,包括语言和言语,使言语分析成为一个可行的诊断工具.
  • 自动语音分析提供了一种具有成本效益的方法来监测患者的认知衰退,并帮助护理人员.

研究的目的:

  • 研究来自自发言语 (SS) 的音频特征在区分认知障碍方面的有效性.
  • 分析语音分析用于认知衰退检测的跨语言适用性.
  • 通过语言和声学数据评估用于预测MCI进展的机器学习模型.

主要方法:

  • 利用来自两个数据集的自发言语 (SS) 和迷你精神状态检查 (MMSE) 成绩:穆格拉大学痴呆门诊 (MUDC) 和阿尔茨海默氏症痴呆通过自发言语 (ADRess) 挑战进行识别.
  • 应用机器学习模型,包括随机森林和多层感知器 (MLP),以分析英语和土耳其语SS的音频特征.
  • 评估了特定语言的模型性能和跨语言的可转移性.

主要成果:

  • 实现了高准确率:ADRess数据集上的随机森林95%,语言特定模型的MUDC数据集上的MLP94%.
  • 当模型应用于其他语言时,英语的准确率为72%,土耳其语的准确率为76%,证明了跨语言的有效性.
  • 突出了音频特征在区分不同语言结构的认知障碍水平方面的重大影响.

结论:

  • 从自发言语中提取的音频特征具有自动认知障碍跟踪的跨语言潜力.
  • 语音分析为临床医生和患者在监测MCI进展方面提供了一种方便且具有成本效益的工具.
  • 这种方法支持早期干预策略,用于预防和管理痴呆症.