Intelligent Prescription Model of Traditional Chinese Medicine Based on Symptom Hypergraph Convolutional Neural Network

The complexity and diversity of clinical cases in Traditional Chinese Medicine (TCM) pose significant challenges for accurate syndrome differentiation and intelligent prescription recommendation. To address these, we propose a novel TCM intelligent prescription model based on a symptom hypergraph convolutional network (HGCN). The model constructs a symptom knowledge network from unstandardized symptom descriptions, builds a symptom hypergraph, and uses HGCN to capture higher-order semantic relationships among symptoms, symptom features, syndromes, and herbs. These relationships are embedded into the hypergraph to train the model for accurate symptom classification and prescription generation. With optimized hyperparameters (e.g., feature embedding dimensions, HGCN layers), the model achieves high accuracy in symptom classification and exceptional prescription recommendation capabilities on specific datasets, demonstrating significant potential for advancing precision medicine in TCM clinical practice.