Identification of key risk factors for venous thromboembolism in urological inpatients based on the Caprini scale and interpretable machine learning methods
The RS model achieved poorer accuracy than the other three common machine learning models. However, the RS model provides strong interpretability and allows for the identification of high-risk factors and decision rules influencing high-risk assessments of VTE in urology. This transparency is very important for clinicians in the risk assessment process.
Authors
- Ching‐Wen Chien (ORCID: https://orcid.org/0000-0001-7260-8427)
- Yanjun Jin
- Yen‐Ching Chuang (ORCID: https://orcid.org/0000-0003-1136-8727)
- Fengmin Cheng
- Chao Liu (ORCID: https://orcid.org/0000-0002-4749-5499)
- Weiying Yang
Institutions
- Hebei North University (CN)
- Wenzhou Medical University (CN)
- University Town of Shenzhen (CN)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- Zhejiang Taizhou Hospital (CN)
- Taizhou University (CN)
Publication Details
- Journal
- Thrombosis Journal
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1186/s12959-024-00645-0
- Citations
- 3
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.65
Funders
- Wenzhou Medical University
- Science and Technology Plan Project of Taizhou