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Long-term Depression01:03

Long-term Depression

2.5K
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...
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Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

65
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...
65
Labeling Emotion01:20

Labeling Emotion

125
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
125
Depression: Overview01:18

Depression: Overview

238
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,...
238
Self-Presentation: Self-Monitoring and Self-Handicapping02:05

Self-Presentation: Self-Monitoring and Self-Handicapping

39.0K
People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about...
39.0K
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

96
Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
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相关实验视频

Updated: Jun 23, 2025

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

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使用X (以前的Twitter) 数据检测暂时的抑郁情绪:语境语言方法.

Ali Akbar Jamali1, Corinne Berger1, Raymond J Spiteri1

  • 1Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.

JMIR AI
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

通过转移学习模型,可以在社交媒体上自动检测瞬间的抑郁情绪. DistilBERT在识别帖子中的抑郁情绪方面表现出卓越的表现,有助于早期干预和公共卫生工作.

关键词:
X (推特) 的意思是抑郁 抑郁症 抑郁症 抑郁症 是一种词汇词典词典词典机器学习是机器学习.暂时的抑郁感 暂时的抑郁感自然语言处理自然语言处理.转移学习转移学习

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科学领域:

  • 计算语言学计算语言学
  • 心理健康信息学心理健康信息学
  • 社交媒体分析.

背景情况:

  • 抑郁症和暂时的抑郁感代表了重大的公共卫生挑战.
  • 早期发现这些感觉对于减少社会负担和提高个人福祉至关重要.
  • 像X (以前的推特) 这样的社交媒体平台提供了大量的数据来理解心理状态和检测抑郁情绪.

研究的目的:

  • 在社交媒体帖子中自动检测瞬间的抑郁情绪.
  • 评估各种机器学习和转移学习模型的有效性.

主要方法:

  • 开发了与抑郁症和暂时抑郁情绪相关的术语词典.
  • 在社交媒体上使用词典. scraped和手动标记帖子.
  • 评估了来自变压器的双向编码器表示 (BERT),A Lite BERT (ALBERT),强大优化的BERT方法 (RoBERTa),蒸的BERT (DistilBERT),卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 和传统机器学习 (ML) 算法的性能.

主要成果:

  • 二元分类在检测抑郁情绪方面表现优于多标签分类.
  • 蒸的BERT在多个指标 (AUC,精度,灵敏度,特异性,精度,F1得分) 上取得了最高的表现.
  • 转移学习算法,特别是DistilBERT,在上下文分析和检测方面明显优于传统的ML算法.

结论:

  • 语境语言方法,特别是转移学习模型,可靠地自动化早期检测瞬间的抑郁情绪.
  • 这些方法可以为社交媒体监控工具的开发提供信息,以识别有风险的个人.
  • 通过这些工具进行主动干预可以减轻抑郁症的社会影响,改善全球公共卫生结果.