| ID | 68711 |
| フルテキストURL | |
| 著者 |
Fukushima, Kazuhiko
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Tsuji, Kenji
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
ORCID
Kaken ID
researchmap
Nakanoh, Hiroyuki
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Uchida, Naruhiko
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Haraguchi, Soichiro
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Kitamura, Shinji
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Kaken ID
publons
Wada, Jun
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
ORCID
Kaken ID
publons
researchmap
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| 抄録 | 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. |
| キーワード | artificial intelligence
case reports
peritoneal dialysis
text mining
|
| 発行日 | 2025-03-26
|
| 出版物タイトル |
Therapeutic Apheresis and Dialysis
|
| 巻 | 29巻
|
| 号 | 3号
|
| 出版者 | Wiley
|
| 開始ページ | 459
|
| 終了ページ | 470
|
| ISSN | 1744-9979
|
| NCID | AA12013945
|
| 資料タイプ |
学術雑誌論文
|
| 言語 |
英語
|
| OAI-PMH Set |
岡山大学
|
| 著作権者 | © 2025 The Author(s).
|
| 論文のバージョン | publisher
|
| PubMed ID | |
| DOI | |
| Web of Science KeyUT | |
| 関連URL | isVersionOf https://doi.org/10.1111/1744-9987.70013
|
| ライセンス | http://creativecommons.org/licenses/by-nc/4.0/
|
| Citation | Fukushima K, Tsuji K, Nakanoh H, Uchida N, Haraguchi S, Kitamura S, et al. Text mining for case report articles on “peritoneal dialysis” from PubMed database. Ther Apher Dial. 2025; 29(3): 459–470. https://doi.org/10.1111/1744-9987.70013
|
| 助成機関名 |
Yukiko Ishibashi Memorial Foundation
Wesco Scientific Promotion Foundation
Japan Society for the Promotion of Science
|
| 助成番号 | 24K11411
22K18229
|
