Validation of a machine learning model for predicting gastrointestinal bleeding in patients with direct oral anticoagulants
ML models, particularly XGBoost, outperformed traditional bleeding risk scores in predicting GIB in DOAC users. However, the performance of the ML models was unsatisfactory. Further research is warranted to achieve a better performance.
Authors
- Joon Sung Kim (ORCID: https://orcid.org/0000-0001-9158-1012)
- Ilsoo Kim (ORCID: https://orcid.org/0000-0003-3090-3086)
- Jong‐Uk Hou (ORCID: https://orcid.org/0000-0002-7101-0244)
- Dae Young Cheung (ORCID: https://orcid.org/0000-0003-4150-3555)
- Jae Hong Choe
- Byung‐Wook Kim (ORCID: https://orcid.org/0000-0002-2290-4954)
Institutions
- Hallym University (KR)
- St. Mary's Hospital (US)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- The Catholic University of Korea Yeouido St. Mary's Hospital (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Scandinavian Journal of Gastroenterology
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1080/00365521.2025.2565321
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00