{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T22:02:46Z","timestamp":1788300166879,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100005851","name":"Shahid Beheshti University of Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005851","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Hepato Biliary Pancreat"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Despite advances in understanding the risk factors of post\u2010endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP), this adverse event remains frequently unpredictable. This study aims to develop a machine learning (ML) model to predict PEP risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were collected from a prospective ERCP registry on patients with na\u00efve papilla who underwent ERCP between 2022 and 2024. CatBoost and eXtreme Gradient Boosting algorithms were trained to estimate PEP risk and the performance of the resulting models was assessed using the area under the receiver operating characteristic (AUC) with 10\u2010fold cross\u2010validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 1330 screened patients, 1190 met the inclusion criteria, and 170 (14.3%) developed PEP. The best\u2010performing algorithm was CatBoost, which consisted of eight features: age, sex, normal papilla morphology, pancreatic duct cannulation, difficult cannulation, abnormal bilirubin levels, common bile duct diameter, and successful stone extraction. This model achieved an AUC of 68.8% (70.4% sensitivity, 67.2% specificity, 26.5% positive predictive value, and 92.0% negative predictive value). The CatBoost model effectively stratified patients into low\u2010, intermediate\u2010, and high\u2010risk groups, with corresponding PEP incidences of 5.7%, 21.0%, and 40.0%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML is highly promising for prediction of PEP. Future studies should focus on multicenter data, inclusion of multimodal data, severity risk\u2010stratification, and real\u2010time application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jhbp.70053","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T07:41:16Z","timestamp":1767685276000},"page":"418-430","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Post\u2010\n                    <scp>ERCP<\/scp>\n                    Pancreatitis Using Machine Learning: Risk Stratification and Feature Importance Analysis"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0557-1278","authenticated-orcid":false,"given":"Erfan","family":"Arabpour","sequence":"first","affiliation":[{"name":"Student Research Committee, Research Institute for Gastroenterology and Liver Diseases Shahid Beheshti University of Medical Sciences  Tehran 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Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Amir Ahmad","family":"Safavi\u2010Naini","sequence":"additional","affiliation":[{"name":"Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases Shahid Beheshti University of Medical Sciences  Tehran Iran"},{"name":"Division of Data\u2010Driven and Digital Medicine, Department of Medicine Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7110-2950","authenticated-orcid":false,"given":"Pardis Ketabi","family":"Moghadam","sequence":"additional","affiliation":[{"name":"Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad 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