焦虑和抑郁症的严重程度分类使用通用焦虑障碍量表和患者健康问卷:国家跨部门研究应用分类和回归树模型
Andre Faro1, Julian Tejada1, Wael Al-Delaimy2
1Departament of Psychology, Federal University of Sergipe, Aracaju, Brazil.
JMIR public health and surveillance
|September 30, 2025
概括
分类和回归树 (CART) 模型创建了简洁的规则来选焦虑和抑郁症严重程度,使用的项目比标准尺度更少. 这些优化的规则提供了高效,准确的心理健康评估,特别是在资源较少的环境中.
科学领域:
- 心理健康 心理健康
- 心理测量 心理测量 心理测量
- 计算统计学 计算统计学
背景情况:
- 可扩展的心理健康查至关重要,特别是在低收入和中等收入国家 (LMICs).
- 现有的工具,如通用焦虑障碍量表 (GAD-7) 和患者健康问卷 (PHQ-9),在大规模使用中面临可行性挑战.
- 较短的版本 (GAD-2,PHQ-2) 减少了负担,但缺乏症状多样性.
研究的目的:
- 以优化使用分类和回归树 (CART) 的焦虑和抑郁查.
- 从GAD-7和PHQ-9项目中确定简洁,高性能的决策规则.
- 为了验证这些规则在独立数据集中的可重复性.
主要方法:
- 一项针对20585名巴西成年人使用数字宣传的大型横截面研究.
- 通过GAD-7和PHQ-9评估的焦虑和抑郁.
- 在启动样本上训练和测试CART模型,使用十倍交叉验证和超参数调整.
主要成果:
- 卡特模型生成了简洁的规则 (GAD-7的2个项目,PHQ-9的3个项目),对于最小/轻度和严重病例 (AUC>0.900) 具有很高的准确性.
- 社会人口统计学变量没有被纳入最终的分类路径.
- 模型在5个独立数据集中显示出稳定的性能和可重现性.
结论:
- 卡特模型为焦虑和抑郁症严重程度分层提供了简化,特异性症状的途径.
- 这些基于规则的模型是固定的简短形式的有效替代品,保留了症状的多样性.
- 结果支持适应性,成本效益的选模型,用于资源有限的环境和LMICs.
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