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相关概念视频

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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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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 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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使用机器学习开发一种名谱,用于预测COVID-19后糖尿病患者的抑郁症.

Haewon Byeon1,2

  • 1Department of Digital Anti-aging Healthcare (BK21), Graduate School of Inje University, Gimhae, Republic of Korea.

Frontiers in public health
|August 3, 2023
PubMed
概括

机器学习在社区糖尿病患者中确定了主要的抑郁风险因素. 早期识别高风险个体对于个性化心理健康支持至关重要.

科学领域:

  • 公共卫生 公共卫生
  • 医疗信息学 医疗信息学
  • 精神病学是一个精神病学.

背景情况:

  • 糖尿病是一种常见的慢性疾病,与抑郁症风险增加有关.
  • 在糖尿病人群中识别抑郁风险因素对于及时干预至关重要.
  • 随着COVID-19的流行,弱势群体面临的心理健康挑战可能会加剧.

研究的目的:

  • 在社区居住的糖尿病患者中确定抑郁症的重大风险因素.
  • 开发和验证预测模型,以识别该群体中患有抑郁症高风险的个体.
  • 为了利用机器学习提高糖尿病抑郁症的预测.

主要方法:

  • 对社区糖尿病患者的大队伍 (26,829名成年人) 的分析.
  • 使用CatBoost机器学习算法来确定变量的重要性.
  • 采用多重物流回归来识别和纠正预测建模中的混因素.

主要成果:

  • 研究人口中抑郁症的患病率为22.4%.
  • 抑郁症的前九大预测因素包括性别,吸烟,酒精/吸烟变化 (COVID-19前/后),主观健康,经济问题,睡眠变化,经济活动和社会支持.
  • 机器学习模型有效地确定了糖尿病患者抑郁症的关键预测因素.
关键词:
在COVID-19大流行中,在 CatBoost 中使用 CatBoost.抑郁 抑郁症 抑郁症 抑郁症 是一种患有糖尿病的患者.机器学习是机器学习.

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结论:

  • 早期识别患有糖尿病和抑郁症的高风险个体至关重要.
  • 建议在初级保健层面建立个性化心理支持系统,以改善心理健康结果.
  • 了解多方面的风险因素,包括与流行病相关的压力因素,是有效管理的关键.