Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning
This study developed a personalized risk prediction model for VTE in PMN patients using machine learning techniques. Additionally, a web-based tool for this predictive model was created. The model demonstrates strong predictive performance and can assist in clinical decision-making for the prevention and treatment of VTE in PMN patients.
Authors
- Qinan Yin (ORCID: https://orcid.org/0000-0001-8560-5751)
- H. Wang (ORCID: https://orcid.org/0009-0003-8329-0145)
- Yin Wang (ORCID: https://orcid.org/0000-0002-8137-7496)
- Yuan Bian (ORCID: https://orcid.org/0000-0003-2365-9177)
- Xingwei Wu
- Liuyun Wu
- Xingyue Zheng (ORCID: https://orcid.org/0000-0003-1062-6202)
- Lian Li (ORCID: https://orcid.org/0000-0002-8517-4661)
- Lizhu Han
Institutions
- University of Electronic Science and Technology of China (CN)
- The Fourth People's Hospital (CN)
- Chengdu Women's and Children's Central Hospital (CN)
- Fourth People's Hospital of Sichuan Province (CN)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2025-11-06
- DOI
- https://doi.org/10.3389/fphar.2025.1683708
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Pharmaceutical Association
- China International Medical Foundation
- Natural Science Foundation of Sichuan Province
- National Key Research and Development Program of China