患者健康问卷-9项配对预测性用于预先查抑郁症状:机器学习分析分析
Darragh Glavin1,2, Eoin Martino Grua1,2, Carina Akemi Nakamura3
1Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland.
JMIR mental health
|October 19, 2023
概括
患者健康问卷-2 (PHQ-2) 可能会比其他两项组合更频繁地错误分类抑郁症. 机器学习确定了PHQ-9项目的更好的配对,用于抑郁症查,这表明无情可能不适合超短评估.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算心理学 计算心理学
- 临床诊断 临床诊断 临床诊断
背景情况:
- 抑郁情绪和无情是主要抑郁障碍的核心症状,构成了查超简短患者健康问卷-2 (PHQ-2) 的基础.
- 来自患者健康问卷-9 (PHQ-9) 与PHQ-2的替代两项配对的比较性能尚未得到充分证实.
- 优化简要查工具对于在临床环境中有效检测抑郁症状至关重要.
研究的目的:
- 采用机器学习 (ML) 来识别和验证PHQ-9项目中最具预测性的抑郁症状2项问卷.
- 通过使用六个外部数据集,评估在不同人群中识别的最佳配对的概括性.
- 为了比较新的配对与已建立的PHQ-2对抑郁症预查的有效性.
主要方法:
- 使用基于ML的逻辑回归模型,研究了PHQ-9中的所有36个可能的2项对联.
- 根据抑郁症症状的分类来评估配对表现 (定义为PHQ-9分数≥10).
- 在一个主数据集和六个外部数据集上验证了表现最好的配对,以确保可概括性.
主要成果:
- 机器学习确定PHQ-9项目2和4 (抑郁情绪和低能耗) 和项目2和8 (抑郁情绪和精神运动变化) 作为优越配对.
- 这些配对 (phq2&4,phq2&8) 显示的曲线下面积 (AUC) 值高于PHQ-2对初级和外部数据集的值.
- 与PHQ-2切断值相比,phq2&4和phq2&8配对显示出更平衡的灵敏度和特异性,数据集之间的性能波动较小.
结论:
- 与其他PHQ-9项目组合相比,标准PHQ-2不一定是预查抑郁症状的最有效工具.
- 当与抑郁情绪相结合时,无情节项目表现不佳,这表明其在超短抑郁症问卷中被纳入可能是经验上有问题的.
- 替代PHQ-9项目配对,特别是phq2&4和phq2&8,可以提供更好的准确性,并减少抑郁症预先查中的错误分类.
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