从PubMed数据库中挖掘关于“腹膜透析”的病例报告文章。
Text mining for case report articles on "peritoneal dialysis" from PubMed database.
作者信息
Fukushima Kazuhiko, Tsuji Kenji, Nakanoh Hiroyuki, Uchida Naruhiko, Haraguchi Soichiro, Kitamura Shinji, Wada Jun
机构信息
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Department of Nephrology, Aoe Clinic, Okayama, Japan.
出版信息
Ther Apher Dial. 2025 Jun;29(3):459-470. doi: 10.1111/1744-9987.70013. Epub 2025 Mar 26.
INTRODUCTION
The number of published medical articles on peritoneal dialysis (PD) has been increasing, and efficiently selecting information from numerous articles can be difficult. In this study, we examined whether artificial intelligence (AI) text mining can be a good support for efficiently collecting PD information.
METHODS
We performed text mining and analyzed all the abstracts of case reports on PD in the PubMed database. In total, 3137 case reports with abstracts related to "peritoneal dialysis" published from 1970 to 2021 were identified.
RESULTS
A total of 280 347 relevant words were extracted from all the abstracts. Word frequency analysis, word dependency analysis, and word frequency transition analysis showed that peritonitis, encapsulating peritoneal sclerosis, and child have been important keywords. Theseanalyses not only reflected historical background but also anticipated future trends of PD study.
CONCLUSION
These suggest that text mining can be a good support for efficiently collecting PD information.
引言
关于腹膜透析（PD）的已发表医学文章数量一直在增加，从众多文章中有效筛选信息可能很困难。在本研究中，我们检验了人工智能（AI）文本挖掘是否能为有效收集PD信息提供良好支持。
方法
我们进行了文本挖掘，并分析了PubMed数据库中所有关于PD的病例报告摘要。总共识别出了1970年至2021年发表的3137篇与“腹膜透析”相关摘要的病例报告。
结果
从所有摘要中总共提取了280347个相关词汇。词频分析、词依存分析和词频转移分析表明，腹膜炎、包裹性腹膜硬化症和儿童一直是重要关键词。这些分析不仅反映了历史背景，还预测了PD研究的未来趋势。
结论
这些结果表明，文本挖掘可为有效收集PD信息提供良好支持。
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