Predicting the Evolution of Depressive Symptoms Using Spatiotemporal Graph Neural Networks - Cavalcante, Gustavo E.; Vignatti, André L. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.20253, 2026)
Network psychopathology has emerged as an innovative paradigm for understanding mental disorders, modeling them as dynamic systems of interacting symptoms. Previous studies have predominantly focused on cross-sectional analyzes of symptom networks. In contrast, this study explores the temporal dimension through the application of Spatio-Temporal Graph Neural Networks (STGNNs) to predict the symptomatic evolution of patients with depressive symptoms. To this end, longitudinal data from the Experience Sampling Method (ESM) of 129 participants were used, with the implementation and comparison of three GNN architectures: Graph Convolutional Network (GCN), Graph Attention Network (GAT) and TransformerConv. These architectures were combined with a temporal layer of GRU in a previously established population network. The results show that TransformerConv achieved the best overall performance (R² = 0.6126), demonstrating a statistically significant result and maintaining stability at different depths and cross-validation folds. This study establishes a methodological basis for the application of STGNNs in psychopathology, offering new tools to predict symptomatic trajectories.