| ID | 68711 |
| FullText URL | |
| Author |
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
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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
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Wada, Jun
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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| Abstract | 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. |
| Keywords | artificial intelligence
case reports
peritoneal dialysis
text mining
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| Published Date | 2025-03-26
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| Publication Title |
Therapeutic Apheresis and Dialysis
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| Volume | volume29
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| Issue | issue3
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| Publisher | Wiley
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| Start Page | 459
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| End Page | 470
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| ISSN | 1744-9979
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| NCID | AA12013945
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| Content Type |
Journal Article
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| language |
English
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| OAI-PMH Set |
岡山大学
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| Copyright Holders | © 2025 The Author(s).
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| File Version | publisher
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| PubMed ID | |
| DOI | |
| Web of Science KeyUT | |
| Related Url | isVersionOf https://doi.org/10.1111/1744-9987.70013
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| License | http://creativecommons.org/licenses/by-nc/4.0/
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| 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
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| Funder Name |
Yukiko Ishibashi Memorial Foundation
Wesco Scientific Promotion Foundation
Japan Society for the Promotion of Science
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| 助成番号 | 24K11411
22K18229
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