Application of machine learning to predict the occurrence of venous thromboembolism in patients hospitalized for coronary artery disease: a single-center retrospective study
The prediction model based on machine learning algorithms for the occurrence of VTE in CHD inpatients has a specific diagnostic value. The prediction model constructed by LR + LASSO and XGBoost is more effective than the models constructed by other methods. The results of this study can provide research ideas for the clinical prevention and treatment of VTE events occurring in CHD inpatients.
Authors
- Zhichao Yang (ORCID: https://orcid.org/0000-0002-7646-9708)
- Jinpeng Yao
- Chen Liu (ORCID: https://orcid.org/0000-0002-9818-0454)
- Xiaohui Wang (ORCID: https://orcid.org/0000-0003-3091-0219)
- Yanan Zhang (ORCID: https://orcid.org/0000-0003-2884-6107)
- Wentao Liu (ORCID: https://orcid.org/0000-0001-6370-4572)
- Jun Wang (ORCID: https://orcid.org/0000-0002-7511-5553)
- Hui He (ORCID: https://orcid.org/0000-0001-7337-9160)
- Yuan-Jiao Yang
- Han-Bing Yan
Institutions
- Xian Central Hospital (CN)
- GDL (China) (CN)
- University of Benin (NG)
- China Development Bank (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-11-28
- DOI
- https://doi.org/10.3389/fcvm.2025.1610938
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00