Modeling symptom-acupoint interactions via a heterogeneous graph learning framework for intelligent acupoint recommendation.

OBJECTIVE: Acupuncture prescriptions involve complex compatibility mechanisms grounded in multi-symptom and multi-acupoint interactions, embodying millennia of clinical experience. Despite growing interest in computational acupoint recommendations, significant challenges persist due to sparse clinical data and the insufficient modelling of symptom-acupoint relationships, posing considerable hurdles to effective prediction. METHODS: We introduced an acupoint compatibility prediction framework with graph neural networks (GNN) and fine-tuned bidirectional encoder representations from transformers (termed GNN-BERT-Attention). The heterogeneous feature interaction learning mechanism was introduced to model symptom-acupoint interactions through heterogeneous graph construction, capturing semantic features and relational patterns in a unified space, which alleviated the sparsity of data. Neural collaborative filtering is utilised via label-aware fusion to iteratively refine the confidence of predictions, while Focal Loss and randomised augmentation strategies enhance robustness against imbalanced label distribution. RESULTS: Comprehensive experiments demonstrate the superiority of the proposed GNN-BERT-Attention model over State-of-the-Art (SOTA) baselines in precision, recall, ranking-based metrics and robustness. Ablation studies validate the effectiveness of each architectural module, and hyperparameter tuning confirms models' stability. A web-based demonstration system further validates clinical applicability, enabling real-time, interpretable acupoint recommendations. CONCLUSION: This study contributes to enhancing the performance of acupoint prediction, ultimately benefiting the efficiency and precision of acupuncture treatment while providing a theoretical foundation for optimising prescriptions and advancing evidence-based traditional Chinese medicine interventions.