Psychologically-Grounded Graph Modeling for Interpretable Depression Detection

2026-04-27

AI Summary

This work addresses the challenges of data scarcity and limited clinical interpretability in conversational depression detection by proposing PsyGAT, a novel framework that models dialogues through dynamic temporal graphs. PsyGAT encodes clinically grounded psychological expression units and incorporates personality-aware graph structures alongside persona-informed data augmentation to disentangle depressive symptoms from underlying personality traits. A key innovation is the Causal-PsyGAT module, which integrates psychological theory to identify symptom-inducing factors, substantially enhancing model interpretability. Evaluated on the DAIC-WoZ and E-DAIC datasets, the proposed approach achieves Macro F1 scores of 89.99 and 71.37, respectively, outperforming existing graph-based models and GPT-5, while improving mean reciprocal rank (MRR) for causal identification by 20%.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• depression detection
• data scarcity
• clinical interpretability
• temporal dynamics
• individual heterogeneity

Innovation

• Psychological Graph Attention Network
• Psychological Expression Units
• personality-aware modeling
• clinically interpretable AI
• causal symptom detection