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Analyzing Medical Semantic Association with Complex Network |
Zhang Junliang1,2(),Fang Xuemei1,Zhang Fan2,Liu Xiwen2,Zhu Peng3 |
1School of Management, Xinxiang Medical University, Xinxiang 453003, China 2Center for Health Information Resources, Xinxiang Medical University, Xinxiang 453003, China 3School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract [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.
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Received: 15 October 2021
Published: 26 October 2022
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Fund:National Social Science Fund of China(21BTQ051);National Social Science Fund of China(17CTQ026);National Natural Science Foundation of China(72174087) |
Corresponding Authors:
Zhang Junliang,ORCID:0000-0002-3678-8691
E-mail: junliangzhang2000@163.com
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