Analysis of original articles and case reports about “hemodialysis” in the Japan Medical Abstracts Society database by text-mining using artificial intelligence

Kitamura Shinji, Nakano Hiroyuki, Morita Takafumi, Takahashi Kensaku, Fukushima Kazuhiko, Tsuji Kenji, Wada Jun

doi:10.4009/jsdt.55.563

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人工知能を用いたテキストマイニングによる「血液透析」に関する医中誌Web収録原著論文および症例事例論文の考察
ジンコウチノウオモチイタテキストマイニングニヨル「ケツエキトウセキ」ニカンスルイチュウシ Web シュウロクゲンチョロンブンオヨビショウレイジレイロンブンノコウサツ

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Abstract

<p>The number of medical manuscripts has increased rapidly since 2000, resulting in a flood of medical information. It is important that humans are able to select appropriate information from this information flood. Here, we text-mined and analyzed the abstracts of original articles and case reports about “hemodialysis” in the Japan Medical Abstracts Society database. We analyzed 26,408 original articles and 8,936 case reports about "hemodialysis", which were published from 1979 to 2020. The total number of words extracted from the abstracts was 1,637,446 for the original articles and 562,949 words for the case reports. There were many studies that examined the extracorporeal circulation method, efficiency, biocompatibility, etc., which were often found during the analysis of the original articles. Furthermore, there were many case reports about treatment difficulties in clinical practice. This study demonstrated that it was possible to analyze the hemodialysis-related case reports included in the Japan Medical Abstracts Society database using the text-mining method and to grasp the changes in clinical hemodialysis treatment that occurred during the study period.</p>

Journal

Nihon Toseki Igakkai Zasshi

Nihon Toseki Igakkai Zasshi 55 (10), 563-571, 2022

The Japanese Society for Dialysis Therapy

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