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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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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.
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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.
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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,...
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检测具有多模式特征的抑郁倾向.

Hui Zhang1, Hong Wang1, Shu Han1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.

Computer methods and programs in biomedicine
|August 2, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了多式变压器抑郁检测 (MTDD) 模型,这是一种用于识别社交媒体用户抑郁症早期迹象的新型神经网络方法. MTDD模型取得了最先进的结果,在检测抑郁倾向方面表现出很高的准确性.

关键词:
深度学习是一种深度学习.抑郁倾向检测 抑郁倾向检测多功能融合融合多功能融合社交媒体 社交媒体

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科学领域:

  • 计算社会科学 计算社会科学
  • 人工智能在心理健康中的作用
  • 自然语言处理用于心理健康分析.

背景情况:

  • 抑郁症显著影响个人福祉和社会健康,需要早期发现和干预.
  • 社交媒体平台提供了丰富的数据来源,用于识别因用户表达情绪和在线寻求支持而导致的抑郁倾向.
  • 现有的抑郁症检测方法通常依赖主观数据或单个模型,限制其准确性和通用性.

研究的目的:

  • 开发和验证一种新的混合神经网络模型,MTDD,用于检测社交媒体用户的抑郁倾向.
  • 为了利用客观,低成本的社交媒体数据来检测抑郁症,克服主观专家咨询和不完整数据集的局限性.
  • 通过整合多式联络功能,提高抑郁症检测模型的稳定性和通用性.

主要方法:

  • 提出了一种混合深度神经网络模型,MTDD,它结合了卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 网络.
  • 利用多式特征,包括文本,语义和域知识,用于向量表示易患抑郁的文本.
  • 开发了一种后级检测方法,分析社交平台上的用户内容.

主要成果:

  • 在检测有抑郁倾向的用户方面,MTDD模型获得了95%的F1得分.
  • 该模型展示了与现有方法相比的最先进 (SOTA) 性能.
  • 广泛的实验证实了该模型的有效性和优势.

结论:

  • MTDD模型提供了一种更有效的方法来检测社交媒体上的抑郁用户,从而促进早期诊断和治疗.
  • 该模型的性能超过了许多最近的抑郁倾向检测模型.
  • 这项研究强调了社交媒体数据和先进的人工智能的潜力,用于心理健康监测.