Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation
This study proposes an accurate model to stratify patients with a high risk of CTEs. The random forest model has good performance. History of ischemic stroke, age, high TG, high TC and high plasma D-Dimer may be correlated with CTEs.
Authors
- Jiefeng Ren (ORCID: https://orcid.org/0000-0001-5388-6704)
- Xinlian Qi
- Hebin Che
- Yi Shao (ORCID: https://orcid.org/0000-0003-1571-2433)
- Kunlian Li
- Wei Gan (ORCID: https://orcid.org/0000-0001-9405-7582)
- Zaiyao Xue
- Quanjin Si (ORCID: https://orcid.org/0000-0001-9548-2302)
- Haijun Wang (ORCID: https://orcid.org/0000-0003-0849-2903)
- Sha Zhang (ORCID: https://orcid.org/0000-0003-1175-6770)
- Jinkun Dai
- Lai Song
- Sai Wang (ORCID: https://orcid.org/0009-0001-9044-7927)
Institutions
- Chinese PLA General Hospital (CN)
- Shandong Provincial QianFoShan Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- BMC Cardiovascular Disorders
- Published
- 2024-08-13
- DOI
- https://doi.org/10.1186/s12872-024-04082-9
- Citations
- 4
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.61