Zou Qunsheng, Wang Yinyan, Shu Zixin, Yang Kuo, Wang Jingjing, Lu Kezhi, Zhu Qiang, Liu Baoyan, Zhang Runshun, Zhou Xuezhong
Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
Evid Based Complement Alternat Med. 2020 Dec 10;2020:8810016. doi: 10.1155/2020/8810016. eCollection 2020.
This study aims to explore the topological regularities of the character network of ancient traditional Chinese medicine (TCM) book. We applied the 2-gram model to construct language networks from ancient TCM books. Each text of the book was separated into sentences and a TCM book was generated as a directed network, in which nodes represent Chinese characters and links represent the sequential associations between Chinese characters in the sentences (the occurrence of identical sequential associations is considered as the weight of this link). We first calculated node degrees, average path lengths, and clustering coefficients of the book networks and explored the basic topological correlations between them. Then, we compared the similarity of network nodes to assess the specificity of TCM concepts in the network. In order to explore the relationship between TCM concepts, we screened TCM concepts and clustered them. Finally, we selected the binary groups whose weights are greater than 10 in (ICH, ) and (TCPD, ), hoping to find the core differences of these two ancient TCM books through them. We found that the degree distributions of ancient TCM book networks are consistent with power law distribution. Moreover, the average path lengths of book networks are much smaller than random networks of the same scale; clustering coefficients are higher, which means that ancient book networks have small-world patterns. In addition, the similar TCM concepts are displayed and linked closely, according to the results of cosine similarity comparison and clustering. Furthermore, the core words of and have essential differences, which might indicate the significant differences of language and conceptual patterns between theoretical and clinical books. This study adopts language network approach to investigate the basic conceptual characteristics of ancient TCM book networks, which proposes a useful method to identify the underlying conceptual meanings of particular concepts conceived in TCM theories and clinical operations.
本研究旨在探索古代中医典籍文字网络的拓扑规律。我们应用二元模型从古代中医典籍构建语言网络。将典籍中的每篇文本拆分为句子,一部中医典籍生成一个有向网络,其中节点代表汉字,链接代表句子中汉字之间的顺序关联(相同顺序关联的出现次数视为该链接的权重)。我们首先计算典籍网络的节点度、平均路径长度和聚类系数,并探究它们之间的基本拓扑相关性。然后,我们比较网络节点的相似度以评估网络中中医概念的特异性。为了探究中医概念之间的关系,我们筛选中医概念并进行聚类。最后,我们在(《黄帝内经》,)和(《伤寒杂病论》,)中选取权重大于10的二元组,希望借此找出这两部古代中医典籍的核心差异。我们发现古代中医典籍网络的度分布符合幂律分布。此外,典籍网络的平均路径长度比相同规模的随机网络小得多;聚类系数更高,这意味着古代典籍网络具有小世界模式。另外,根据余弦相似度比较和聚类结果,相似的中医概念紧密展示并相互关联。而且,《黄帝内经》和《伤寒杂病论》的核心词汇存在本质差异,这可能表明理论典籍和临床典籍在语言及概念模式上存在显著差异。本研究采用语言网络方法来研究古代中医典籍网络的基本概念特征,为识别中医理论和临床实践中特定概念的潜在概念意义提供了一种有用的方法。