【目的】探索一種易于用戶理解的規范化、自動化聚類結果判讀和表達方式,促進主題詞共現聚類的發展。【方法】以腫瘤診斷主題為例,參考標引教材梳理相關的主題詞/副主題詞標引規則,選取10組腫瘤為訓練集進行高頻主題詞共現聚類分析,人工審讀聚類結果,結合標引規則,梳理高頻主題詞語義類型/副主題詞組合規則。基于規則編寫Python程序,自動解讀驗證集中4組腫瘤的聚類結果,并請專家對其揭示類團內容的準確性、全面性、實用性、易理解性和簡潔性進行評價。【結果】整理標引規則30條,梳理面向主題詞共現聚類結果解讀的語義類型/副主題詞組合規則98條。驗證集的5個評價指標(準確性、全面性、實用性、易理解性和簡潔性)分值分別為4.282、4.435、4.209、4.457、4.206(滿分5分)。【局限】探索語義類型/副主題詞組合規則時,研究結果與每次聚類過程中高頻閾值的選擇、聚類結果數的確定均有關聯。利用組合規則解讀類團內容難以揭示類團"隱藏信息"。【結論】基于規則自動解讀主題詞共現聚類分析結果具有較強適用性,在一定程度上促進了主題詞共現聚類分析結果表達的客觀化與規范化。

[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.