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 disease treatment and healthcare through patienti-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), an novel framework that effectively combines molecular-scale features and macroscopic properties of herbs with clinical symptoms, and provides the refined representation of their multiscale associations, enhancing the effectiveness of TCM formula recommendation. This framework can integrate molecular-scale chemical features and macroscopic properties of herbs, and capture complex local and global relations in the heterogeneous graph of symptoms and herbs, providing the effective embedding representation of their multiscale features and associations in a unified semantic space. Based on the refined feature representation, the framework is not only compatible with both traditional unordered formula recommendation task and the ordered herb sequence generation task, but also improves model's performance in both tasks. Comprehensive evaluations demonstrate FMASH's superior performance on the TCM formula recommendation over the state-of-the-art (SOTA) baseline, achieving relative improvements of 9.45\% in Precision@5, 12.11% in Recall@5, and 11.01% in F1@5 compared to the SOTA model on benchmark datasets. This work facilitates the practical application of AI-based TCM formula recommendation system.