Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling

The in-depth analysis of the semantic information contained in Traditional Chinese Medicine (TCM) prescriptions is of great significance for both clinical applications and the discovery of new formulas. Existing TCM prescription generation algorithms define the interactions between all symptom- herb pairs solely based on co-occurrence, without considering the categorization of herbal properties. To address this issue, this paper proposes a Prescription Recommendation Algorithm based on Herbal Property- Driven Compatibility Mechanism Semantic Modeling (HPDCM). First, the analysis of prescriptions takes into account the herbal property categories, which are defined as entities when constructing the knowledge graph. Second, the algorithm integrates compatibility rules to model the interactions between symptoms and herbs with weighted connections. This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. Experimental results, compared with existing formula generation algorithms, demonstrate that HPDCM achieves higher accuracy and is more in line with the TCM diagnostic and therapeutic principles of syndrome differentiation and treatment.