A study on a real-world data-based VTE risk prediction model for lymphoma patients
This study established and validated a machine learning model for predicting VTE risk in lymphoma patients, with the optimal model demonstrating excellent discriminatory ability (AUC = 0.954). The model provides evidence to guide the timing and strategy of anticoagulation, supporting early VTE screening and risk stratification in clinical practice. Its implementation has important implications for improving patient outcomes and advancing public health.
Authors
- Qinan Yin (ORCID: https://orcid.org/0000-0001-8560-5751)
- S S Li
- Yin Wang (ORCID: https://orcid.org/0000-0002-6973-856X)
- Xuewu Song (ORCID: https://orcid.org/0000-0003-4750-7151)
- Gang Li (ORCID: https://orcid.org/0000-0003-3522-3231)
- Han Zhang (ORCID: https://orcid.org/0000-0002-0166-1973)
- Yuan Bian (ORCID: https://orcid.org/0000-0003-2365-9177)
- Lizhu Han
- Fu-Hong Cai
- Fengjiao Kang
- Cong He
Institutions
- University of Electronic Science and Technology of China (CN)
- Xinjiang Medical University (CN)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2025-10-14
- DOI
- https://doi.org/10.3389/fphar.2025.1691271
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Pharmaceutical Association
- Natural Science Foundation of Sichuan Province