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@INPROCEEDINGS{Villatoro-Tello_TSD2026_2026,
                      author = {Villatoro-Tello, Esa{\'{u}} and Reyes-Ram{\'{\i}}rez, Ernesto A. and Burdisso, Sergio and Watawana, Hasindri Sankalpana and Motlicek, Petr and S{\'{a}}nchez-Vega, Fernando and L{\'{o}}pez-Monroy, A. Pastor},
                    keywords = {Clinical Interviews, depression detection, digital mental health, Graph Neural Networks, Interpretability, topic modelling},
         mainresearchprogram = {AI for Everyone},
                       month = sep,
                       title = {Modelling Thematic Structure in Clinical Dialogues for Interpretable Depression Detection},
                   booktitle = {Text, Speech, and Dialogue. TSD 2026. Lecture Notes in Computer Science},
                        year = {2026},
                         url = {https://link.springer.com/chapter/10.1007/978-3-032-37249-9_47},
                         doi = {https://doi.org/10.1007/978-3-032-37249-9_47},
                    abstract = {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}
}