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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".