Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach
The stacking machine learning model based on EGFR mutation and clinical characteristics had a predictive value for postoperative VTE in patients with NSCLC.
Authors
- Yonghui Di (ORCID: https://orcid.org/0009-0006-0056-8933)
- Ligang Hao (ORCID: https://orcid.org/0000-0003-2294-8778)
- Junjie Zhang (ORCID: https://orcid.org/0000-0003-2710-8516)
- Peng Zhang (ORCID: https://orcid.org/0000-0002-6881-9800)
- Qi Zheng (ORCID: https://orcid.org/0000-0003-1431-4724)
Institutions
- Hebei Medical University (CN)
- Xingtai People's Hospital (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1371/journal.pone.0320674
- Citations
- 2
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 3.74