Modelling Thematic Structure in Clinical Dialogues for Interpretable Depression Detection

Detecting depression from clinical interviews requires models that capture discourse-level thematic structure rather than relying solely on lexical features. We propose Topic-GCN, a heterogeneous graph convolutional network that jointly encodes word, latent topic, and document nodes, enabling explicit modelling of topic–document associations for interview-level classification. The learned topic nodes surface interpretable symptom-relevant clusters whose class-conditional relevance can be mapped to established psycholinguistic categories. Experiments on the DAIC-WOZ and E-DAIC corpora show that Topic-GCN improves predictive performance over state-of-the-art methods while preserving interpretability. A post-hoc analysis using LIWC categories shows that the learned topic structure aligns with relevant symptom dimensions (Code: https://github.com/idiap/Topic-GCN_for_depression_detection).