Automatic Expression of Co-occurrence Clustering Based on Indexing Rules of Medical Subject Headings
Wu Jinming1,Hou Yuefang2,Cui Lei2()
1Institute of Medical Information/Medical Library, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100020, China 2College of Medical Informatics, China Medical University, Shenyang 110122, China
[Objective] This study proposes an automatic procedure to present the clustering results, aiming to promote the development of co-word clustering analysis.[Methods] First, we examined the indexing rules of neoplastic diagnosis and chose 10 common neoplasms as sample sets for co-occurrence clustering analysis. Then, we reviewed the results and combined the indexing rules to identify the semantic types / subheading combination patterns of high-frequency subject headings. Third, we developed a python application to automatically interpret the clustering results for four groups of neoplasms. Finally, we invited 12 experts to evaluate the accuracy, comprehensiveness, practicality, comprehensibility and simplicity of the presentation.[Results] We found 30 indexing patterns of neoplastic diagnosis as well as 98 combination semantic patterns. The scores of the accuracy, comprehensiveness, practicality, comprehensibility and simplicity were 4.282, 4.435, 4.209, 4.457, and 4.206 out of 5.[Limitations] It was difficult to reveal the “hidden relations” among the subject headings with the proposed method.[Conclusions] Our new method could effectively present results of co-occurrence clustering analysis for medical records.
邬金鸣,侯跃芳,崔雷. 基于医学主题词标引规则的词共现聚类分析结果自动判读和表达的研究[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
Wu Jinming,Hou Yuefang,Cui Lei. Automatic Expression of Co-occurrence Clustering Based on Indexing Rules of Medical Subject Headings. Data Analysis and Knowledge Discovery, 2020, 4(9): 133-144.
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