{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:39:11Z","timestamp":1780544351080,"version":"3.54.1"},"reference-count":51,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015649","name":"Yukiko Ishibashi Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015649","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ther Apher Dial"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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 \u201cperitoneal dialysis\u201d published from 1970 to 2021 were identified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 280\u2009347 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>These suggest that text mining can be a good support for efficiently collecting PD information.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1744-9987.70013","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T20:09:01Z","timestamp":1743278941000},"page":"459-470","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Text mining for case report articles on \u201cperitoneal dialysis\u201d from <scp>PubMed<\/scp> database"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5403-8906","authenticated-orcid":false,"given":"Kazuhiko","family":"Fukushima","sequence":"first","affiliation":[{"name":"Department of Nephrology, Rheumatology, Endocrinology and Metabolism Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences  Okayama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Tsuji","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Rheumatology, Endocrinology and Metabolism Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences  Okayama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Nakanoh","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Rheumatology, Endocrinology and Metabolism Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences  Okayama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naruhiko","family":"Uchida","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Rheumatology, Endocrinology and Metabolism Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences  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