ArticleThrombosis journal2024
Identification of key risk factors for venous thromboembolism in urological inpatients based on the Caprini scale and interpretable machine learning methods.
Article in Thrombosis journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Caprini score for venous thromboembolism risk assessment: A scoping review of applications, validation, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Geriatric assessment-integrated prediction model for postoperative deep vein thrombosis in older adults undergoing transurethral resection of the prostate: retrospective cohort study.Frontiers in medicine · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
purposeTo identify the key risk factors for venous thromboembolism (VTE) in urological inpatients based on the Caprini scale using an interpretable machine learning method.
methodsVTE risk data of urological inpatients were obtained based on the Caprini scale in the case hospital. Based on the data, the Boruta method was used to further select the key variables from the 37 variables in the Caprini scale. Furthermore, decision rules corresponding to each risk level were generated using the rough set (RS) method. Finally, random forest (RF), support vector machine (SVM), and backpropagation artificial neural network (BPANN) were used to verify the data accuracy and were compared with the RS method.
resultsFollowing the screening, the key risk factors for VTE in urology were "(C
conclusionsThe 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.
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