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

Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

444
Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
444
Depression: Overview01:18

Depression: Overview

794
Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
794
Long-term Depression01:05

Long-term Depression

33.1K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
33.1K
Long-term Depression01:03

Long-term Depression

3.1K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
3.1K

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

Evaluating Patient Safety and Efficacy of Robot-Assisted Adrenalectomy: 90-Day Outcomes.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP·2026
Same author

Rarely reported cases of hepatotoxicity associated with turmeric- and curcuminoid-containing dietary supplements: a comprehensive review by USP.

Pharmaceutical biology·2026
Same author

Cotton Salt Stress Resilience: Integrating Physiological, Molecular, and Agronomic Strategies for Next-Generation Breeding.

Plant, cell & environment·2026
Same author

Computational multi-omics modelling identifies TOP2A as a central prognostic biomarker and therapeutic target in renal cell carcinoma.

Archives of biochemistry and biophysics·2026
Same author

Giant transverse magnetic fluctuations at the edge of re-entrant superconductivity in UTe<sub>2</sub>.

Nature communications·2026
Same author

Chemical and hydrostatic pressure induced metallization in [Formula: see text] [Formula: see text] single crystals.

Scientific reports·2026

相关实验视频

Updated: Jan 16, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K

可解释的AI驱动的抑郁症检测从社交媒体使用自然语言处理和黑机器学习模型.

Sidra Hameed1, Muhammad Nauman1, Nadeem Akhtar2

  • 1Faculty of Computing, The Islamia University of Bahawalpur, Punjab, Pakistan.

Frontiers in artificial intelligence
|September 29, 2025
PubMed
概括

这项研究表明,支持矢量机器 (SVM) 可以从社交媒体上准确地检测抑郁症. 像LIME这样的可解释AI (XAI) 方法为模型决策提供了洞察力,提高了早期心理健康检测的可信度.

关键词:
地方可解释的模型不可知论解释 (LIME)可解释的人工智能机器学习是机器学习.心理疾病检测 精神疾病检测自然语言处理自然语言处理.

更多相关视频

Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.7K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

3.2K

相关实验视频

Last Updated: Jan 16, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.7K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

3.2K

科学领域:

  • 计算精神病学是一种计算精神病学.
  • 人工智能在心理健康中的作用

背景情况:

  • 精神疾病,特别是抑郁症,给个人和社会带来了巨大的负担.
  • 社交媒体为计算心理健康研究提供了丰富的用户生成数据来源.
  • 早期发现抑郁症对于及时干预和改善结果至关重要.

研究的目的:

  • 在社交媒体数据上使用机器学习 (ML) 模型探索早期发现抑郁症.
  • 整合可解释AI (XAI) 方法,以提高黑盒ML模型的解释性.
  • 评估ML和XAI在抑郁症检测方面的联合预测性能和可解释性.

主要方法:

  • 使用的黑盒ML模型:支持矢量机器 (SVM),随机森林 (RF),极端梯度增强 (XGB) 和人工神经网络 (ANN).
  • 采用自然语言处理 (NLP) 技术,包括TF-IDF,LDA,N-grams,BoW和GloVe嵌入用于特征提取.
  • 综合局部可解释模型-不可知论解释 (LIME) 提供对模型预测的洞察力.

主要成果:

  • 支持矢量机器 (SVM) 在检测来自社交媒体内容的抑郁症方面表现出最高的准确性.
  • LIME成功地为模型预测提供了详细的解释,识别了关键的语言标记.
  • 识别的语言标记与已建立的抑郁症心理研究一致.

结论:

  • 这项研究强调了SVM在社交媒体数据中抑郁症检测的有效性.
  • 整合LIME显著提高了ML模型的可解释性和临床可靠性.
  • 将预测准确度与可解释性结合起来,对于推进心理健康领域的计算方法至关重要.