Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding
The findings in this study enable effective and timely preventive interventions for high-risk patients, and help avoid unnecessary monitoring in low-risk patients.
Authors
- Yu-Lu Qin
- Lan Li (ORCID: https://orcid.org/0000-0003-3877-1030)
- Chao Lü (ORCID: https://orcid.org/0000-0002-1265-0903)
- Ke-Fang Sun
- Yide Zhou
- Hao-Yang Cheng
- Lei Xu
- Chao-Hui Yu
- Ren-Ke Zhu
- Jiao-E Chen
- Jian-Zhong Sang
Institutions
- Quzhou College of Technology (CN)
- Rochester General Hospital (US)
- Quzhou University (CN)
- Zhejiang Provincial People's Hospital (CN)
- Yuxian People's Hospital (CN)
- Ningbo First Hospital (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- World Journal of Gastroenterology
- Published
- 2026-01-16
- DOI
- https://doi.org/10.3748/wjg.v32.i3.115527
- Primary Topic
- Gastrointestinal Bleeding Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00