基于语言的机器学习抑郁症检测:系统审查和元分析
Hadar Fisher1, Nigel M Jaffe1, Kristina Pidvirny1
1McLean Hospital.
Research square
|December 3, 2025
概括
使用自然语言处理 (NLP) 和机器学习 (ML) 来自动检测抑郁症显示出有希望的结果,平均准确率为0.80. 性能根据使用的语言,数据源和功能而有很大差异.
科学领域:
- 计算语言学计算语言学
- 精神病学是一个精神病学.
- 人工智能的人工智能
背景情况:
- 早期发现抑郁症对于有效的干预至关重要.
- 自然语言处理 (NLP) 和机器学习 (ML) 提供自动化方法来从文本中检测抑郁症.
- 关于这些自动化方法的诊断性能的证据有限.
研究的目的:
- 系统地审查和分析NLP和ML的研究,以从口语或书面语言中检测抑郁症.
- 评估整体诊断性能,并确定影响准确性的因素.
主要方法:
- 研究的系统审查和元分析.
- 搜索了六个电子数据库和其他来源,包括123篇全文文章.
- 50项独立研究的定量综合,分析了聚合精度,精度,回忆,AUC和平衡精度.
主要成果:
- 在43项研究中,聚合精度为0.80 (95% CI,0.76-0.83).
- 分组分析显示,基于语言,文本源,特征类型和分类器 (p < .001) 的性能存在显著差异.
- 结构化临床采访,非英语语言以及基于语言或嵌入的特征的准确性最高.
结论:
- 从文本中自动检测抑郁症显示出有希望的性能,但具有相当大的异质性.
- 性能受语言,数据源,特征提取和模型类型的影响.
- 临床应用需要方法标准化和验证.
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