用小组判断和调查变量来预测焦虑程度的新方法
Sumra Bari1, Byoung-Woo Kim1, Nicole L Vike1
1Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA.
Npj mental health research
|June 18, 2024
概括
这项研究开发了一个机器学习框架,使用15个判断变量和上下文数据,以81%的准确度预测焦虑水平. 这些发现突出了关键预测因素和更高焦虑的行为概况,有助于心理健康评估.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 心理健康 心理健康
背景情况:
- 焦虑症影响着数以百万计的人,造成严重的痛苦和损伤.
- 机器学习模型已被探索用于预测焦虑特征.
- 现有的模型往往缺乏广泛的心理健康评估的解释性和可扩展性.
研究的目的:
- 开发一种可解释的机器学习框架,用于预测焦虑程度.
- 识别判断变量,上下文因素和焦虑之间的关系.
- 利用可访问的数据创建一个可扩展的心理健康评估系统.
主要方法:
- 从基于智能手机的图片评分任务中使用了15个可解释的判断变量 (例如,风险回避).
- 纳入了包括人口统计,孤独感和COVID-19历史在内的上下文变量.
- 在3476名美国成年人的数据上使用了平衡的随机森林模型.
主要成果:
- 在预测国家特征焦虑清单 (STAI) 衍生的焦虑水平方面达到高达81%的准确性和0.71 AUC ROC.
- 确定年龄,孤独,收入和就业作为关键的上下文预测因素.
- 判断变量占预测重要性的高达61%,揭示了高焦虑个体中较少的弹性和更多的回避的个人资料.
结论:
- 可以使用可解释的判断和上下文变量构建一个可扩展的心理健康评估系统.
- 了解焦虑变异背后的心理过程可以为治疗的发展提供信息.
- 这种方法提供了一种有效的方法来识别和潜在地干预焦虑状况.
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