数字中医药（英文）2022，Vol.5Issue(4)：419-428,10.DOI:10.1016/j.dcmed.2022.12.007
基于图卷积网络的《伤寒论》异质图构建及节点表示学习方法
Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network
摘要
Abstract
Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) dataset and explore an optimal learning meth-od represented with node attributes based on graph convolutional network (GCN).Methods Clauses that contain symptoms, formulas, and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs, which were used to propose a node representation learning method based on GCN - the Traditional Chinese Medicine Graph Convolution Network (TCM-GCN). The symptom-formula, symp-tom-herb, and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes, and thus acquiring the nodes' sum-aggregations of symp-toms, formulas, and herbs to lay a foundation for the downstream tasks of the prediction models. Results Comparisons among the node representations with multi-hot encoding, non-fusion encoding, and fusion encoding showed that the Precision@10, Recall@10, and F1-score@10 of the fusion encoding were 9.77％, 6.65％, and 8.30％, respectively, higher than those of the non-fusion encoding in the prediction studies of the model. Conclusion Node representations by fusion encoding achieved comparatively ideal results, indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.关键词
图卷积网络/异质图/《伤寒论》/异质图节点表示/节点表示学习Key words
Graph convolutional network (GCN)/Heterogeneous graph/Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》)/Node representations on heterogen-eous graph/Node representation learning引用本文复制引用
晏峻峰,文志华,邹北骥..基于图卷积网络的《伤寒论》异质图构建及节点表示学习方法[J].数字中医药（英文）,2022,5(4):419-428,10.基金项目
New-Generation Artificial Intelligence-Major Program in the Sci-Tech Innovation 2030 Agenda from the Min-istry of Science and Technology of China(2018AAA0102100),Hunan Provincial Department of Education key project(21A0250),and The First Class Dis-cipline Open Fund of Hunan University of Traditional Chinese Medicine(2022ZYX08). （2018AAA0102100）
