CL-MHAD: Contrastive Learning-based Multi-Hypergraph Aggregation and Diffusion model for prescription recommendation

Multiple syndrome-based prescription recommendations are significant for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM). However, it remains a challenge to effectively extract and fuse multi-dimensional knowledge in herbs for accurate syndrome-based prescription recommendations. To address it, we propose CL-MHAD, a contrastive learning-based multi-hypergraph aggregation and diffusion model for prescription recommendation. Focusing on prescriptions, herbs, properties, and dosages, this model presents a multi-view hypergraph reconstruction mechanism, which employs a consistent Top-K neighbor strategy to generate affinity and feature matrices. Subsequently, a diffusion-enhanced method is proposed, utilizing multi-step propagation and weighted mapping to capture high-order relationships. Then, we propose a cross-view contrastive learning strategy to enhance discrimination and present a topology-aware random walk augmentation approach to alleviate data sparsity. An integrated loss function combining classification, contrastive, and augmentation losses is designed to optimize model performance. We evaluate CL-MHAD in a real-world clinical setting using Gastrointestinal Diseases (GID) as a case study. Extensive experiments, including comparative evaluations, ablation studies, and parameter sensitivity analyses, show that CL-MHAD achieves overall superior performance compared with representative baselines across multiple evaluation metrics. Specifically, the model achieves performance gains of 1.27% to 24.67% over representative baselines across all datasets. We further evaluate the model through dosage-syndrome interaction and data-sparsity robustness tests, with results demonstrating the effectiveness of our weighted fusion strategy and the model's superior stability. This study offers an effective solution and a promising paradigm for accurate syndrome-based prescription recommendations in TCM.