Multi-label classification of visceral diseases based on human acupoint networks via local-global graph neural networks
Bibliographic record
Abstract
Identifying acupoint-disease associations is of paramount significance. However, existing approaches generally suffer from key limitations: they either overlook potential heterogeneous correlations among samples or fail to fully exploit unstructured individual-specific features. To address these issues, we innovatively construct a novel human acupoint network using mutual information between acupoints, enabling the discovery of previously unexplored latent topological dependencies. A community detection method is then applied to partition the network into structurally consistent local subgraphs. Furthermore, a novel multimodal local-global Acupoint Graph Neural Network (LGA-GNN) is proposed for multi-label classification of visceral diseases. The LGA-GNN framework comprises two core components. The local ACU-GNN module learns structural embeddings from the partitioned subgraphs and identifies key acupoint nodes indicative of visceral diseases. The global Subject-GNN module integrates local embeddings with nonacupoint data (gender, age) to capture heterogeneous inter-individual relationships relevant to disease labels. To enhance the discriminative power of local embeddings, a Feature-Guided Propagation Pooling (FGP-Pooling) mechanism is incorporated into the ACU-GNN to retain diagnostically significant node features. Meanwhile, the Subject-GNN employs a forward information aggregation module to adaptively fuse multi-scale graph features, improving the model’s representation capacity and classification performance. The proposed LGA-GNN is comprehensively evaluated on a large-scale real-world dataset. Experimental results achieve an average AUC of 0.94 and ACC of 0.88 under five-fold cross-validation. The model significantly outperforms state-of-the-art baselines across multiple metrics, demonstrating strong effectiveness and superiority in multi-label disease classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".