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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Psychological responses to stress encompass the various cognitive and emotional reactions individuals experience when faced with challenging or threatening situations, such as a job loss. Prolonged exposure to stressors can disturb emotional balance, increasing negative emotions (e.g., anxiety and sadness) and diminishing positive emotions (e.g., joy and satisfaction). These persistent emotional shifts are associated with an increased risk of both physical illness and mental health issues, such...
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相关实验视频

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在主观响应错误中预测抑郁和焦虑时评估机器学习稳定性

Wai Lim Ku1, Hua Min2

  • 1Systems Biology Center, National Heart, Lung and Blood Institute, NIH, Bethesda, MD 20892, USA.

Healthcare (Basel, Switzerland)
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概括

机器学习模型可以预测主要抑郁症 (MDD) 和泛性焦虑症 (GAD),但主观调查数据引入了不准确性. 一个卷积神经网络 (CNN) 显示出优越的弹性和准确性与不可靠的心理健康数据.

关键词:
算法偏差是一种算法偏差.数据干扰的数据干扰.电子健康记录是电子医疗记录.机器学习是机器学习.心理健康预测 心理健康预测稳定的稳定性 稳定的稳定性调查数据分析调查数据分析

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科学领域:

  • 计算精神病学是一种计算精神病学.
  • 机器学习在医疗保健中的应用
  • 心理健康信息学心理健康信息学

背景情况:

  • 大型抑郁症 (MDD) 和泛性焦虑症 (GAD) 显著影响个人和社会.
  • 准确预测MDD和GAD对于及时干预和治疗至关重要.
  • 使用电子健康记录和调查数据的机器学习 (ML) 模型显示出预测这些条件的前景,但容易受到主观数据不准确的影响.

研究的目的:

  • 评估五种ML算法的可靠性,以预测MDD和GAD在不同程度的主观调查响应不准确的情况下.
  • 识别ML算法,证明弹性和保持预测准确性,当面对数据不可靠性.

主要方法:

  • 评估了五种ML算法:卷积神经网络 (CNN),随机森林,XGBoost,后勤回归和天真贝叶斯.
  • 使用了一个包含生物医学,人口统计和自我报告调查信息的数据集.
  • 模拟主观反应的不准确性 (记忆回忆偏差,主观解释) 来测试算法性能.

主要成果:

  • 所有算法都在高质量的调查数据上表现良好.
  • 当遇到错误或有偏见的答案时,表现有显著差异.
  • 美国有线电视新闻网表现出卓越的弹性,保持甚至提高准确性,科恩的卡帕,以及MDD和GAD预测的正确性.

结论:

  • 算法弹性对于准确的心理健康预测至关重要,尤其是在主观的自我报告数据中.
  • 美国有线电视新闻网 (CNN) 显示出强大的能力来处理心理健康预测中的数据不可靠性.
  • 仔细的算法选择是必不可少的,CNN成为预测MDD和GAD在数据不确定性下有希望的候选人.