CONF Villatoro-Tello_TSD2026_2026/IDIAP Modelling Thematic Structure in Clinical Dialogues for Interpretable Depression Detection Villatoro-Tello, Esaú Reyes-Ramírez, Ernesto A. Burdisso, Sergio Watawana, Hasindri Sankalpana Motlicek, Petr Sánchez-Vega, Fernando López-Monroy, A. Pastor Clinical Interviews depression detection digital mental health Graph Neural Networks Interpretability topic modelling Text, Speech, and Dialogue. TSD 2026. Lecture Notes in Computer Science 2026 https://link.springer.com/chapter/10.1007/978-3-032-37249-9_47 URL https://doi.org/10.1007/978-3-032-37249-9_47 doi 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