{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T11:11:02Z","timestamp":1791544262244,"version":"4.3.4"},"reference-count":42,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"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":["Br J Haematol"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmb\u00f3lica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3\u2009months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE\u2010BLEED scores were used for comparisons. External validation was performed with the COMMAND\u2010VTE database. Learning was carried out with data from 49\u2009587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE\u2010BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE\u2010BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE\u2010BLEED scores only in the prospective validation cohort, but not in the external validation cohort.<\/jats:p>","DOI":"10.1111\/bjh.18737","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T09:26:50Z","timestamp":1679390810000},"page":"971-981","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations"],"prefix":"10.1111","volume":"201","author":[{"given":"Dami\u00e1n","family":"Mora","sequence":"first","affiliation":[{"name":"Department of Internal Medicine Hospital Virgen de la Luz  Cuenca Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Mateo","sequence":"additional","affiliation":[{"name":"Institute of Technology, Universidad de 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