{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T15:22:17Z","timestamp":1747149737471,"version":"3.40.5"},"reference-count":36,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["JGH Open"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aim<\/jats:title><jats:p>Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a single\u2010center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12\u2009months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, na\u00efve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out\u2010of\u2010sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver\u2010related deaths were analyzed using Kaplan\u2013Meier analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Compared to logistic regression (AUC\u2009=\u20090.595, <jats:italic>P<\/jats:italic>\u2009=\u20090.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC\u2009=\u20090.84) and XGB (AUC\u2009=\u20090.83) models; however, the AUC was not significantly high for decision tree (AUC\u2009=\u20090.633) or na\u00efve Bayes (AUC\u2009=\u20090.584) models. Kaplan\u2013Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log\u2010rank\u2009=\u20090.005 and 0.007).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jgh3.12915","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T13:15:29Z","timestamp":1685711729000},"page":"431-438","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning prediction model for treatment responders in patients with primary biliary cholangitis"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-1219","authenticated-orcid":false,"given":"Naruhiro","family":"Kimura","sequence":"first","affiliation":[{"name":"Division of Gastroenterology and Hepatology Niigata University Graduate School of Medical and Dental Sciences  Niigata Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuya","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Division 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