通过融合听觉,视觉和文字线索,基于深度学习的抑郁症检测
Chenyang Xu1, Yangbin Chen2, Yanbao Tao3
1Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 100191 Beijing, China.
Journal of affective disorders
|July 17, 2025
概括
这项研究开发了一个人工智能模型用于使用视觉,听觉和文本数据检测抑郁症. 多式联网方法实现了高准确性,优于单一数据类型,并在聊天机器人采访中显示出前景.
科学领域:
- 人工智能的人工智能
- 计算精神病学是一种计算精神病学.
- 机器学习 机器学习
背景情况:
- 早期发现抑郁症对于及时干预至关重要.
- 视觉,声学和语义信号的自动化分析正在与深度学习一起推进.
- 目前用于抑郁症评估的方法可能是耗时和主观的.
研究的目的:
- 提出一个自动化抑郁症检测模型,整合视觉,听觉和文本数据.
- 在各种场景中验证模型的性能,包括聊天机器人交互.
- 评估多模式抑郁症检测模型的通用性.
主要方法:
- 开发了一个GPT-2.0驱动的聊天机器人用于症状调查.
- 采访期间捕获的音频视频和文字数据,并补充了简短的情感采访任务.
- 采用多头交叉注意网络并进行外部验证的融合多式联络功能.
主要成果:
- 多式模式模型在内部验证中实现了高精度 (AUC>0.950,精度>0.930).
- 在聊天机器人面试场景中观察到异常的表现 (AUC = 0.999).
- 外部验证显示了良好的概括性 (AUC = 0.978),尽管性能略有降低.
结论:
- 多式人工智能模型显示了精确检测抑郁症的巨大潜力.
- 需要进一步研究纵向研究和适用于严重抑郁症病例的研究.
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