利用多式联络方法来检测抑郁症,使用大型语言模型和面部表情
Misha Sadeghi1, Robert Richer2, Bernhard Egger3
1Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 91052, Germany. misha.sadeghi@fau.de.
Npj mental health research
|December 23, 2024
概括
这项研究开发了一种自动化方法,通过采访记录和面部数据来预测抑郁症的严重程度. 基于文本的分析,增强语音质量,被证明是最有效的准确的抑郁症检测.
科学领域:
- 心理健康 心理健康
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
背景情况:
- 准确的抑郁症检测对于有效的心理健康治疗至关重要.
- 自动化方法可以提高抑郁症评估的效率和可访问性.
研究的目的:
- 开发和评估一种全新的,完全自动化的方法来预测抑郁症的严重程度.
- 为了比较基于文本,面部和多式模式的模型来预测抑郁症的严重程度.
主要方法:
- 使用了包括采访成绩单和视频数据的E-DAIC数据集.
- 使用大型语言模型 (LLM) 来从文本中提取抑郁症指标.
- 集成的文本和面部特征用于多式联络分析.
- 训练有素的预测模型使用患者健康问卷-8 (PHQ-8) 的得分.
主要成果:
- 通过通过语音质量评估增强文本数据来实现最佳性能.
- 纯文本模型显示出强大而有效的抑郁症严重性预测.
- 对于最佳模型,报告了2.85的平均绝对误差 (MAE) 和4.02的根平均平方误差 (RMSE).
结论:
- 自动抑郁症检测对心理健康诊断具有重大潜力.
- 基于文本的分析,特别是当与语音质量增强时,是预测抑郁症严重程度的可行方法.
- 多模式方法为改善抑郁症评估准确性提供了未来的途径.
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