Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Dementia01:30

Dementia

591
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
591

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Variability in epilepsy polygenic risk prediction across Taiwanese population and clinical cohorts.

Epilepsia·2026
Same author

Correction to: Differential striatal dopamine binding in Parkinson's Disease with and without REM sleep behavior disorder: A Tc‑99 m TRODAT‑1 SPECT study.

GeroScience·2026
Same author

Therapeutic effects of 40 Hz light stimulation on clinical and pathological features of Alzheimer's disease.

Dialogues in clinical neuroscience·2026
Same author

Using Plasma Amyloid Beta Oligomer to Screen in Alzheimer's Disease: A Pilot Study.

International journal of molecular sciences·2026
Same author

Treatment persistence with acetylcholinesterase inhibitors in Alzheimer's disease: Real-world evidence from a retrospective cohort study.

Journal of Alzheimer's disease : JAD·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

相关实验视频

Updated: Feb 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

基于机器学习的痴呆症查工具的开发和验证:六个问题痴呆症查测试

Meng-Tien Wu1, Kuan-Ying Li2,3, Ching-Fang Chien2,3,4

  • 1School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.

American journal of Alzheimer's disease and other dementias
|February 17, 2026
PubMed
概括

一个新的机器学习工具,六个问题痴呆症查测试 (6Q-DS),有效地选痴呆症. 这种快速,用户友好的方法提供了高准确度,有助于早期检测,并减少认知能力下降的负担.

关键词:
认知障碍是一种认知障碍.痴呆症 痴呆症是一种痴呆症.早期检测 早期检测机器学习是机器学习.神经心理评估神经心理评估

更多相关视频

Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test
08:07

Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test

Published on: November 23, 2019

11.9K
The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease
06:23

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

Published on: October 13, 2016

33.9K

相关实验视频

Last Updated: Feb 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test
08:07

Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test

Published on: November 23, 2019

11.9K
The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease
06:23

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

Published on: October 13, 2016

33.9K

科学领域:

  • 神经学 神经学
  • 老年学是一门学科.
  • 人工智能在医学中的应用

背景情况:

  • 及时发现痴呆症对于减轻健康和社会影响至关重要.
  • 现有的查工具,如迷你心理状态检查 (MMSE) 和认知能力查仪器 (CASI),由于时间和资源的限制而面临限制.

研究的目的:

  • 开发和验证基于机器学习的痴呆症查工具.
  • 评估六个问题痴呆查测试 (6Q-DS) 作为快速查方法的有效性.

主要方法:

  • 使用 eXtreme 梯度提升开发机器学习模型.
  • 利用了台湾神经病诊所的533名老年人的数据.
  • 采用六个问题的痴呆症查测试 (6Q-DS),采访六项,用于收集数据.

主要成果:

  • 6Q-DS在区分痴呆症和非痴呆症方面表现出很高的表现,AUC为0.936,灵敏度为0.879,特异性为0.951,准确度为0.907.
  • 对于识别非常轻度痴呆症,6Q-DS的AUC值为0.874,灵敏度为0.818,特异性为0.805,准确度为0.810.
  • 性能与MMSE和CASI等既有工具相提并论.

结论:

  • 6Q-DS是用于痴呆症查的实用,快速和用户友好的工具.
  • 机器学习应用程序提高了痴呆症检测的效率.
  • 6Q-DS显示出作为传统查方法的替代方案的希望.