基于心理的图模型用于可解释的抑郁症检测
Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
AI总结：
本文提出PsyGAT模型，通过动态时序图建模对话，结合心理表达单元和人格上下文，提升抑郁症检测的可解释性与性能，达到SOTA水平。
摘要：
本文提出PsyGAT模型，通过动态时序图建模对话，结合心理表达单元和人格上下文，提升抑郁症检测的可解释性与性能，达到SOTA水平。
英文摘要：
Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or account for participant-specific heterogeneity. In this work, we propose PsyGAT (Psychological Graph Attention Network), a psychologically grounded framework that models conversational sessions as dynamic temporal graphs. We introduce Psychological Expression Units (PEUs) to explicitly encode utterance-level clinical evidence, structuring the session graph to capture transitions in psychological states rather than mere semantic dependencies. To address the critical class imbalance in depression datasets, we employ clinically approved persona-based data augmentation, enable robust model learning. Additionally, we integrate session-level personality context directly into the graph structure to disentangle trait-based behavior from acute depressive symptoms. PsyGAT achieves state-of-the-art performance, surpassing both strong graph-based baselines and closed-source LLMs like GPT-5, achieving 89.99 and 71.37 Macro F1 scores in DAIC-WoZ and E-DAIC, respectively. We further introduce Causal-PsyGAT, an interpretability module that identifies symptom triggers. Experiments show a 20% improvement in MRR for identifying causal indicators, effectively bridging the gap between depression monitoring and clinical explainability. The full augmented dataset is publicly available at https://doi.org/10.6084/m9.figshare.31801921.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
