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相关概念视频

Psychological Responses to Stress01:20

Psychological Responses to Stress

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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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一个协同细分算法来预测从被动感知mHealth数据的情绪压力.

Younghoon Kim1,2, Sumanta Basu1, Samprit Banerjee2

  • 1Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.

Statistics in medicine
|May 19, 2025
PubMed
概括
此摘要是机器生成的。

一个新的算法使用智能手机数据来检测情绪障碍患者的情绪压力. 与传统的机器学习方法相比,这种数据驱动的方法可以更好地识别压力时期.

关键词:
变化点检测 变化点检测这是分类分类的分类.移动健康 移动健康 移动健康 移动健康机器学习是机器学习.心理健康 心理健康压力检测 压力检测

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科学领域:

  • 计算精神病学是一种计算精神病学.
  • 数字健康数字健康
  • 机器学习用于医疗保健

背景情况:

  • 中年和老年人的情绪障碍和慢性疼痛是复杂的,通常受到情绪压力的影响.
  • 传统的机器学习 (ML) 方法很难从被动感测和自我报告的变量中捕获非静止时间序列数据中的时间变化的模式.
  • 识别短期压力波动对于有效的治疗干预至关重要.

研究的目的:

  • 利用被动感知和自我报告的智能手机数据开发数据驱动的共分类算法,用于识别情绪压力状态.
  • 通过分析时间变化的局部模式来提高检测压力时期的准确性.
  • 为了利用不同数据类型之间的短时间窗口关联,用于预测建模.

主要方法:

  • 开发了一种新的共细分算法,通过检测变化点来细分被动感应变量.
  • 检查了被动感应和主动 (自我报告) 变量之间的特定细分市场的关联.
  • 使用标准的ML方法,利用已识别的共分段时期来预测未来的情绪压力状态.
  • 将算法应用于ALACRITY第一阶段研究中的患者数据.

主要成果:

  • 数据驱动的细分算法准确地识别了情绪压力时期.
  • 与不包含细分的传统ML方法相比,提出的方法在检测压力时期方面表现出更高的准确性.
  • 该算法有效地捕获时间变化的局部模式和短时间窗口关联,这对于压力检测至关重要.

结论:

  • 数据驱动的细分提供了一种更准确的方法来识别情绪障碍患者的情绪压力时期.
  • 开发的算法增强了来自数字健康工具的复杂,非静止时间序列数据的分析.
  • 这种方法有望改善数字精神卫生保健中的监测和干预策略.