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Accurate and comprehensive documentation is crucial for enhancing patient safety and optimizing clinical outcomes; however, adverse events remain underreported. This study evaluates a machine learning-based approach for systematically detecting endoscopic adverse events from real-world clinical metadata, including structured hospital data such as ICD-codes and procedure timings. Using a random forest classifier detecting adverse events perforation, bleeding, and readmission, we analysed 2490 inpatient cases, achieving significant improvements over baseline prediction accuracy. The model achieved AUC-ROC\/AUC-PR values of 0.9\/0.69 for perforation, 0.84\/0.64 for bleeding, and 0.96\/0.9 for readmissions. Results highlight the importance of multiple metadata features for robust predictions. This semi-automated method offers a privacy-preserving tool for identifying documentation discrepancies and enhancing quality control. By integrating metadata analysis, this approach supports better clinical decision-making, quality improvement initiatives, and resource allocation while reducing the risk of missed adverse events in endoscopy.<\/jats:p>","DOI":"10.1038\/s41746-025-01826-5","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T15:07:05Z","timestamp":1752160025000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["How machine learning on real world clinical data improves adverse event recording for endoscopy"],"prefix":"10.1038","volume":"8","author":[{"given":"Stefan","family":"Wittlinger","sequence":"first","affiliation":[]},{"given":"Isabella C.","family":"Wiest","sequence":"additional","affiliation":[]},{"given":"Mahboubeh Jannesari","family":"Ladani","sequence":"additional","affiliation":[]},{"given":"Jakob Nikolas","family":"Kather","sequence":"additional","affiliation":[]},{"given":"Matthias P.","family":"Ebert","sequence":"additional","affiliation":[]},{"given":"Fabian","family":"Siegel","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Belle","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"1826_CR1","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/S0002-9610(01)00589-X","volume":"181","author":"SM Kavic","year":"2001","unstructured":"Kavic, S. 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I.W. received honoraria from AstraZeneca. J.K. declares consulting services for Bioptimus, France; Panakeia, UK; AstraZeneca, UK; and MultiplexDx, Slovakia. Furthermore, he holds shares in StratifAI, Germany, Synagen, Germany, Ignition Lab, Germany; has received an institutional research grant by G.S.K.; and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, Merck, MSD, BMS, Roche, Pfizer, and Fresenius. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"424"}}