Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity
This paper proposes Logic-GNN, a neuro-symbolic framework that treats clinical records as a structured private language governed by latent logical games. By integrating Temporal Graph Neural Networks with Graph Kolmogorov Complexity, it induces a symbolic grammar representing medical interaction logic, defining anomalies as grammatical violations that expand the Minimum Description Length of the clinical graph. Evaluated on the Sina System dataset (2M+ records), Logic-GNN achieves an F1-score of 0.94, outperforming state-of-the-art baselines by 12% in distinguishing life-threatening medical outliers from data corruption. The approach introduces a self-healing mechanism to suggest logical corrections for real-time data integrity.
[2605.15242] Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity
[Submitted on 14 May 2026]
Title:Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity
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Abstract:The reliability of Healthcare Information Systems (HIS) is frequently compromised by human-induced data entry errors, which existing statistical anomaly detection methods fail to distinguish from legitimate clinical extremes. This paper proposes Logic-GNN, a novel neuro-symbolic framework that treats clinical records as a structured `private language'' governed by latent logical games. By integrating Temporal Graph Neural Networks (TGNN) with Graph Kolmogorov Complexity, we induce a symbolic grammar that represents the underlying logic of medical interactions. We define anomalies as `grammatical violations'' that cause a significant expansion in the Minimum Description Length (MDL) of the clinical graph. Evaluated on the Sina System dataset (2M+ records), Logic-GNN achieves an F1-score of 0.94, outperforming state-of-the-art baselines by 12\% in distinguishing between life-threatening medical outliers and data corruption. Our approach introduces a self-healing mechanism that suggests logical corrections to maintain data integrity in real-time HIS environments.
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Machine Learning (cs.LG)
Cite as: arXiv:2605.15242 [cs.LG]
(or arXiv:2605.15242v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.15242
arXiv-issued DOI via DataCite
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From: Abolfazl Zarghani [view email] [v1] Thu, 14 May 2026 06:19:43 UTC (12 KB)
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