用注意力机制融合领域知识，让抑郁程度评估更准确且可解释。
AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment
- 通过跨注意力融合心理健康知识图谱，增强文本上下文理解。
- 在多个数据集上提升超过5%的分级F1分数，优于现有方法。
- 适合关注心理健康的AI可解释性研究者与临床辅助工具开发者。
在当今互联社会中，社交媒体平台为洞察个体思想、情绪和心理状态提供了窗口。本文探讨利用Facebook、X（原Twitter）和Reddit等平台进行抑郁严重程度检测。提出AttentionDep模型，一种基于域感知注意力的方法，通过融合上下文与领域知识实现可解释的抑郁严重程度估计。采用一元词和二元词对帖子进行分层编码，注意力机制突出临床相关词汇。通过交叉注意力机制引入经筛选的心理健康知识图谱中的领域知识，丰富上下文特征。最终使用有序回归框架预测抑郁严重程度，尊重临床意义及严重等级的自然顺序。实验表明，AttentionDep在多个数据集上的分级F1分数超越现有最优基线超过5%，同时提供可解释的预测依据。该研究推动了社交媒体心理状态评估中可信透明AI系统的发展。
原文摘要 · Abstract (English)
In today's interconnected society, social media platforms provide a window into individuals' thoughts, emotions, and mental states. This paper explores the use of platforms like Facebook, X (formerly Twitter), and Reddit for depression severity detection. We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation by fusing contextual and domain knowledge. Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms highlighting clinically relevant tokens. Domain knowledge from a curated mental health knowledge graph is incorporated through a cross-attention mechanism, enriching the contextual features. Finally, depression severity is predicted using an ordinal regression framework that respects the clinical-relevance and natural ordering of severity levels. Our experiments demonstrate that AttentionDep outperforms state-of-the-art baselines by over 5% in graded F1 score across datasets, while providing interpretable insights into its predictions. This work advances the development of trustworthy and transparent AI systems for mental health assessment from social media.
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