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

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

487
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
487
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

193
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
193

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

From crisis to resilience: Catalysing epidemic and pandemic preparedness through National Public Health Institutes.

SSM - health systems·2026
Same author

Auto-assessment of assessment: A human-in-the-loop AI framework addressing policy gaps in academic assessment.

PloS one·2026
Same author

Exploring the feasibility of near-infrared spectroscopy and machine learning for detecting cardiovascular diseases and diabetes mellitus in fingernails.

The Analyst·2026
Same author

Autonomous vehicles with augmented reality internet of things and edge intelligence system for industry 5.0 based on 6G.

PloS one·2025
Same author

An Explainable Framework for Mental Health Monitoring Using Lightweight and Privacy-Preserving Federated Facial Emotion Recognition.

Sensors (Basel, Switzerland)·2025
Same author

Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.

BMC medical informatics and decision making·2025

相关实验视频

Updated: Jul 4, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

一种可解释的机器学习方法用于阿尔茨海默氏症疾病分类.

Abbas Saad Alatrany1,2,3,4, Wasiq Khan5, Abir Hussain6,7

  • 1School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK. a.s.alatrany@2020.ljmu.ac.uk.

Scientific reports
|February 1, 2024
PubMed
概括

机器学习模型使用国家阿尔茨海默氏症协调中心数据准确预测阿尔茨海默氏症 (AD) 风险和进展. 可解释的AI方法识别了诸如记忆和判断等关键因素,有助于早期诊断和了解AD发展.

更多相关视频

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

7.5K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

相关实验视频

Last Updated: Jul 4, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
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

7.5K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

科学领域:

  • 神经学 神经学
  • 计算生物学 计算生物学
  • 生物统计学 生物统计学

背景情况:

  • 由于微妙的生物标志物变化,早期阿尔茨海默病 (AD) 诊断具有挑战性.
  • 机器学习 (ML) 提供了识别AD风险的潜力,但往往缺乏解释性.
  • 高维度和有限的数据集在AD研究中带来了挑战.

研究的目的:

  • 开发和验证可解释的ML模型,用于早期AD诊断和进展预测.
  • 通过可解释的ML方法识别导致AD发展的关键因素.
  • 为了利用大规模的数据集进行强大的AD风险评估.

主要方法:

  • 使用国家阿尔茨海默氏症协调中心数据集 (169,408条记录,1024个特征) 减少了特征空间.
  • 训练支持向量机 (SVM) 模型用于二进制 (NC与NC对比) 的模型. AD) 和多类分类和进展预测.
  • 采用规则提取技术 (类规则挖掘,稳定和可解释的规则集) 和SHAP/LIME用于模型解释性.

主要成果:

  • SVM模型实现了高性能:98.9%的F1分数用于二进制分类和90.7%的多类分类.
  • SVM准确地预测了AD的进展 (88%F1对于二进制,72.8%对于多类).
  • 可解释的人工智能确定了关键因素:记忆,判断,共同点,方向和临床痴呆症评级工具.

结论:

  • 可解释的ML模型在AD诊断和进展预测方面表现出高准确度.
  • 确定了关键的认知和临床因素,对于理解AD发展至关重要.
  • 该研究强调了可解释的ML在阿尔茨海默病临床决策支持中的有用性.