Open Access

ARTICLE

Neuro-Symbolic Graph Learning for Causal Inference and Continual Learning in Mental-Health Risk Assessment

Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

1 Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India
2 Department of Architecture and Architectural Engineering, Yonsei University, Seoul, 03722, Republic of Korea
3 Department of Industrial Security, Chung-Ang University, Seoul, 06974, Republic of Korea

* Corresponding Authors: Noman Khan. Email: ; Mi Young Lee. Email:

Computer Modeling in Engineering & Sciences 2026, 146(1), 44 https://doi.org/10.32604/cmes.2025.075119

Received 25 October 2025; Accepted 19 December 2025; Issue published 29 January 2026

Abstract

Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph Learning (CNSGL) framework that unifies symbolic reasoning, causal inference, and continual learning within a single architecture. Each post is represented as a symbolic graph linking clinically relevant tags to textual content, enriched with causal edges derived from directional Point-wise Mutual Information (PMI). A two-layer Graph Convolutional Network (GCN) encodes these graphs, and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances. Continual adaptation across datasets is achieved through the Multi-Head Freeze (MH-Freeze) strategy, which freezes a shared encoder and incrementally trains lightweight task-specific heads (small classifiers attached to the shared embedding). Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews, demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability. Across six datasets, MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score, with AUPRC ≥0.934 and AUROC ≥0.942, consistently surpassing all continual-learning baselines. The results confirm the framework’s ability to preserve prior knowledge, adapt to domain shifts, and maintain causal interpretability, establishing CNSGL as a promising step toward robust, explainable, and lifelong mental-health risk assessment.

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Keywords

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