基于复杂网络的医学语义关联研究

Analyzing Medical Semantic Association with Complex Network

【目的】运用复杂网络理论和技术构建语义关联网络,研究医学语义关联。【方法】以医学语义概念为复杂网络节点,语义关联为边,构建医学语义关联网络,并分析其网络特征和语义社区,同时运用深度学习进行语义概念向量化和语义聚类分析。【结果】将PubMed中MEDLINE的Coronavirus文献作为数据集,构建包含43个节点和877条边的医学语义关联网络,对网络特征、语义社区和语义聚类进行验证和可视化展示。【局限】实验数据较少。【结论】语义关联网络可以有效表达医学概念间的语义关联,为医学知识发现服务提供参考。

[Objective]This paper aims to study medical semantic association with the help of complex network.[Methods]First,we constructed a medical semantic association network using the medical semantic concepts as nodes and semantic associations as edges.Then,we analyzed the network characteristics and semantic community.Finally,we created vectors for the semantic concepts and conducted semantic clustering analysis with the neural network.[Results]We retrieved relevant literature on“coronavirus”from MEDLINE of PubMed and built a semantic association network with 43 nodes and 877 edges.Then,we visualized the network characteristics,semantic community and semantic clusters.[Limitations]The experimental data size needs to be expanded.[Conclusions]The proposed network effectively describes the semantic association among medical concepts and benefits medical knowledge discovery services.