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相关概念视频

Depression: Overview01:18

Depression: Overview

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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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Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

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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.
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...
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Long-term Depression01:05

Long-term 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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Depressive Disorders: MDD and Dysthymia01:27

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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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使用智能手机识别抑郁症的快速和最小系统:可解释的基于机器学习的方法

Md Sabbir Ahmed1, Nova Ahmed1

  • 1Design Inclusion and Access Lab, North South University, Dhaka, Bangladesh.

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|August 10, 2023
PubMed
概括

这项研究开发了一种快速,极简的系统,使用应用程序使用数据在一秒钟内检测抑郁症. 机器学习模型实现了82.4%的准确性,为低资源设置提供了潜在的解决方案.

科学领域:

  • 数字化表型化是指数字化表型化.
  • 机器学习在心理健康中的应用
  • 计算精神病学是一种计算精神病学.

背景情况:

  • 现有的抑郁症检测系统需要随着时间的推移进行广泛的数据收集,这限制了它们在早期干预方面的有效性.
  • 当前的方法可能是资源密集型的,在低资源环境中是不可行的.

研究的目的:

  • 创建一个极简的系统,使用最少的数据快速识别抑郁症.
  • 识别和解释最有效的机器学习模型来检测抑郁症.

主要方法:

  • 开发了一种工具,可以在1秒内从100名孟加拉学生那里收集7天的应用使用数据.
  • 采用各种机器学习模型 (线性,基于树的,神经网络) 具有特征选择 (过器,包装器,嵌入式) 和嵌套交叉验证.
  • 使用Shapley添加式解释 (SHAP) 来实现模型的解释性.

主要成果:

  • 一个增强光度梯度的机器模型在使用1秒应用程序使用数据识别抑郁的学生时达到82.4%的准确性.
  • 采用~5个Boruta选择的特征的堆叠模型达到77.4%的精度和77.9%的平衡精度.
  • 日常应用程序使用模式比汇总数据更能表明抑郁症;SHAP分析揭示了与抑郁症相关的特定行为标记.
关键词:
抑郁 抑郁症 抑郁症 抑郁症 是一种可以解释的机器学习低资源设置中的低资源设置实时系统实时系统.智能手机的智能手机智能手机的智能手机.学生 学生 学生 学生

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结论:

  • 这种快速,简单的系统在欠发达和发展中国家的抑郁症检测方面表现有前途.
  • 这些发现可以指导开发更少的资源密集型系统,以了解和干预学生抑郁症.