通过主题协作与交互反馈，提升抑郁症筛查对话的自动检测准确率
Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration
- 引入上下文学习识别对话主题，建模主题内与主题间关联
- 在DAIC-WOZ数据集上比顶尖方法提升35%和12%准确率
- 支持临床医生干预调整主题权重，适合医疗辅助诊断场景
自动抑郁症检测可为临床早期干预提供线索。抑郁症筛查访谈通常围绕多个主题展开对话。现有研究多采用端到端神经网络模型捕捉访谈对话的层次结构，但存在两大缺陷：一是未能显式建模主题内的相关性与主题间的关联性；二是无法让临床医生主动介入并聚焦特定主题。为此，本文提出一种交互式抑郁症检测框架。该框架利用上下文学习技术识别访谈中的主题，并建模主题内与主题间的相关性；同时，通过人工智能驱动的反馈模拟临床医生的兴趣，实现对主题重要性的交互式调整。在抑郁症检测数据集DAIC-WOZ上，该方法相比当前最优模型分别实现了35%和12%的绝对性能提升，验证了建模主题相关性与引入交互外部反馈的有效性。
原文摘要 · Abstract (English)
Automatic depression detection provides cues for early clinical intervention by clinicians. Clinical interviews for depression detection involve dialogues centered around multiple themes. Existing studies primarily design end-to-end neural network models to capture the hierarchical structure of clinical interview dialogues. However, these methods exhibit defects in modeling the thematic content of clinical interviews: 1) they fail to capture intra-theme and inter-theme correlation explicitly, and 2) they do not allow clinicians to intervene and focus on themes of interest. To address these issues, this paper introduces an interactive depression detection framework. This framework leverages in-context learning techniques to identify themes in clinical interviews and then models both intra-theme and inter-theme correlation. Additionally, it employs AI-driven feedback to simulate the interests of clinicians, enabling interactive adjustment of theme importance. PDIMC achieves absolute improvements of 35\% and 12\% compared to the state-of-the-art on the depression detection dataset DAIC-WOZ, which demonstrates the effectiveness of modeling theme correlation and incorporating interactive external feedback.
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