基于PHQ对齐症状总结的临床访谈中可解释多模态抑郁识别
Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization
AI总结：
该研究针对临床访谈多模态抑郁识别的可解释性不足问题，提出Explain-MDRC框架，构建含PHQ-8对齐注释的Explain-DAIC数据集，开发PhqCML模型，提升识别性能并提供可解释中间证据。
摘要：
近期临床访谈多模态抑郁识别（MDRC）领域的进展显示，整合文本、声学和面部线索的AI系统具备应用潜力。但现有方法对可解释性关注有限，制约了可复现性与临床医生审查。为解决该问题，我们提出Explain-MDRC，这是一种模仿临床工作流程的可解释MDRC框架，通过从文本生成结构化症状总结并将其与非语言线索整合以进行识别。具体而言，我们构建了Explain-DAIC，该数据集基于DAIC-WOZ构建，并补充了PHQ-8对齐的总结注释，为开发内置可解释性的模型提供基础。我们进一步提出PhqCML模型，该模型结合了PHQ-8对齐的症状总结、感知PHQ的对比学习以及总结驱动的多模态融合。自动指标与专家评估显示，Explain-MDRC提升了识别性能，并提供了更具可解释性、临床医生可读的中间证据，为透明的AI辅助抑郁识别研究指明了有前景的方向。
英文摘要：
Recent advances in multimodal depression recognition for clinical interviews (MDRC) have demonstrated the potential of AI systems by integrating textual, acoustic, and facial cues. However, existing methods pay limited attention to interpretability, thereby constraining reproducibility and clinician review. To address this, we introduce Explain-MDRC, an explainable MDRC framework that mirrors clinical workflows by generating structured symptom summaries from text and integrating them with nonverbal cues for recognition. Specifically, we construct Explain-DAIC, a dataset based on DAIC-WOZ and enriched with PHQ-8-aligned summary annotations, providing a foundation for developing models with built-in interpretability. We further propose PhqCML, a model that combines PHQ-8-aligned symptom summarization with PHQ-aware contrastive learning and summary-informed multimodal fusion. Automated metrics and expert evaluations show that Explain-MDRC improves recognition performance and provides more interpretable, clinician-readable intermediate evidence, suggesting a promising direction for transparent AI-assisted depression recognition research.
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