Development and comparative evaluation of machine learning models for predicting lower extremity deep vein thrombosis in gastrointestinal cancer patients using multicenter longitudinal clinical data
Machine learning models derived from multicenter, longitudinal clinical datasets offer robust predictive capabilities for assessing DVT risk in patients with gastrointestinal malignancies. These models furnish clinicians with individualized risk stratification tools, facilitating the refinement of preventive strategies and the enhancement of clinical decision-making, ultimately contributing to improved patient management.
Authors
- Songyun Zhao (ORCID: https://orcid.org/0000-0003-0542-1085)
- Yuan Liu (ORCID: https://orcid.org/0000-0002-2098-8509)
- Zhiyang Jiang
- Jing Xu (ORCID: https://orcid.org/0000-0001-7126-1652)
- Jin Xia (ORCID: https://orcid.org/0000-0002-3084-1981)
- Yanfei Zhu (ORCID: https://orcid.org/0000-0003-0148-4601)
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Plastic Surgery Hospital (CN)
- Tengzhou Central People's Hospital (CN)
- Wuxi People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Surgery
- Published
- 2025-10-10
- DOI
- https://doi.org/10.3389/fsurg.2025.1648645
- Citations
- 1
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.87