{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:10:14Z","timestamp":1777500614165,"version":"3.51.4"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Acute respiratory failure (ARF) and atrial fibrillation (AF) are common diseases. This study established a predictive model for the risk of atrial fibrillation in patients with ARF, aiming to provide tools for clinical application.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study examined the data of 21,594 patients in the MIMIC-IV database, including factors such as age, vital signs, and laboratory results on the first day of admission. Six feature selection techniques and six machine learning algorithms were used to construct the prediction model, and then the prediction model was verified using the MIMIC-III database. Evaluate the performance of the model through the comparison of results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 59 predictor variables were identified, among which age was the most important factor. These variables are used to establish predictive models. The verification results show that the XGBoost model (AUC: 0.816) and the Random Forest (RF) model (AUC: 0.822) have the best performance. This study presents the first predictive model for atrial fibrillation in patients with acute respiratory failure.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Both the XGBoost and RF models demonstrated outstanding performance. These findings will make significant contributions to the diagnosis of clinical complications and the resolution of public health issues.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2025.1696609","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T05:28:36Z","timestamp":1759987716000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting atrial fibrillation in patients with acute respiratory failure using machine learning: application of the MIMIC-III and MIMIC-IV datasets"],"prefix":"10.3389","volume":"12","author":[{"given":"Rixuan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40560-023-00658-3","article-title":"Guideline-based management of acute respiratory 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