State-element-aware Syndrome Classification Based on Hypergraph Convolutional Network

Syndrome classification plays a key role in the clinical diagnosis and treatment with traditional Chinese medicine (TCM), aiming to identify the disease type. Given symptoms of a patient, existing approaches for syndrome classification are generally limited to modeling the interaction between symptoms and syndromes while ignoring the induction of state elements. To alleviate this issue, a state-element-aware hypergraph convolutional network (SEHGCN) is proposed to incorporate state elements into syndrome classification and discover high-order semantic relationships among TCM entities through hypergraph convolutional network (HGCN). Specifically, state elements are initially induced from symptoms by an extraction network, then symptoms and state elements are embedded via convolution on patient hypergraph to obtain the latent representation of patients. Finally, syndromes are classified by a multilayer perceptron (MLP). Extensive experiments on two TCM datasets show that the syndrome classification results with this proposed method are significantly improved over other competing methods.