Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024
We developed a Random Forest model based on patients' clinical data, which effectively predicts the risk of thrombosis in arteriovenous fistulas. SHAP analysis offers the potential to inform personalized and evidence-based nursing interventions for healthcare professionals.
Authors
- Ruben Yiqi Luo (ORCID: https://orcid.org/0000-0003-4483-8369)
- Peng Shu (ORCID: https://orcid.org/0000-0001-8945-8402)
- Xia Wang (ORCID: https://orcid.org/0000-0001-5655-2291)
- Fang Xu (ORCID: https://orcid.org/0000-0002-2326-6288)
- Ling Huang (ORCID: https://orcid.org/0000-0002-0999-4326)
- Zhuping Wen
Institutions
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- BMC Nephrology
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1186/s12882-025-04201-4
- Citations
- 4
- Primary Topic
- Central Venous Catheters and Hemodialysis
- Type
- article
- Field-Weighted Citation Impact
- 10.07