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Multi-hypergraph convolutional neural network for herb recommendation
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DOI：10.1016/j.bspc.2026.111386.png)
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Abstract
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As the core of Traditional Chinese Medicine (TCM) diagnosis and treatment, herb recommendation aims to recommend a group of herbs according to patients’ symptoms. Existing graph-based methods mainly model pairwise relations and either omit explicit syndrome and state-element knowledge or represent the common induction of multiple patients through independent pairwise edges, thereby failing to model it as a single high-order relation. To address this limitation, we aim to improve herb recommendation by explicitly modeling the high-order patient relations induced by shared syndromes and state-element configurations. We propose a multi-hypergraph convolutional neural network (MHGCN) that represents each patient as a node and constructs a syndrome-induced patient hypergraph and a state-element-induced patient hypergraph. In each hypergraph, one hyperedge connects all patients sharing the same syndrome or exact state-element configuration. Parallel encoders learn from the two hypergraphs, and their representations are fused for multi-label herb prediction using a frequency-weighted binary cross-entropy loss. Across ten runs, MHGCN achieved mean F1-score@5 values of 70.532% on the Treatise on Febrile Diseases dataset and 29.737% on the Dictionary of Traditional Chinese Medicine Prescriptions dataset, respectively. It outperformed all evaluated topic-model, graph-based, and large language model baselines. In paired analyses using matched random seeds, its improvements over LightGCN M in Precision@5 and F1-score@10 remained significant under both paired t -tests and Wilcoxon signed-rank tests after Holm correction ( p adj < 0.05 ). These findings demonstrate that representing each common induction event as a patient hyperedge enables MHGCN to model syndrome- and state-element-induced high-order relations directly and supports its effectiveness for herb recommendation.
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