FMASH: Advancing Traditional Chinese Medicine Formula Recommendation with Efficient Fusion of Multiscale Associations of Symptoms and Herbs
Traditional Chinese medicine (TCM) exhibits remarkable therapeutic efficacy in healthcare through patient-specific formulas. However, current AI-based TCM formula recommendation models and methods mainly focus on data-based textual associations between symptoms and herbs, and have not fully utilized their features and relations at different scales, especially at the molecular scale. To address these limitations, we propose the Fusion of Multiscale Associations of Symptoms and Herbs (FMASH), a novel framework that effectively incorporates the properties of herbs on different scales with clinical symptoms and provides refined embeddings of their multiscale associations. The framework integrates molecular-scale features and macroscopic properties of herbs and combines complex local and global relations in the heterogeneous graph of symptoms and herbs. Moreover, it provides effective representation embeddings of the multiscale features and associations of symptoms and herbs in a unified semantic space. Comprehensive experiments have been conducted on FMASH, and the results demonstrate that our FMASH-based model outperforms the state-of-the-art (SOTA) model on both datasets, confirming the effectiveness of FMASH in building the TCM formula recommendation model. In Dataset1, our model has achieved a significant improvement compared to the SOTA model, with increases of 3.38% in Precision@5, 3.89% in Recall@5, and 3.69% in F1-score@5. In Dataset2, Precision@5, Recall@5, and F1-score@5 increase by 2.64%, 1.92%, and 2.23%, respectively. This work facilitates the application of the AI-based TCM formula recommendation and promotes the innovative development of TCM diagnosis and treatment.