ArticleAsia-Pacific journal of oncology nursing2025
Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: A retrospective and prospective cohort study.
Article in Asia-Pacific journal of oncology nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Merging artificial intelligence into cancer nursing care: Current applications, challenges, and opportunities.Asia-Pacific journal of oncology nursing · 2026Article
- Mechanobiology of cancer-associated thrombosis: from molecular mechanisms to therapeutic innovation.Journal of nanobiotechnology · 2026Review
- Pulmonary origin and risk assessment of cancer-associated thrombosis.Thrombosis journal · 2026Review
- AI-enabled D-dimeromics in precision breast oncology: a transformative framework for the identification, stratification, and prognostication of ultra-high-risk disease phenotypes.Frontiers in oncology · 2026Review
- Machine learning-based prediction of postoperative venous thromboembolism in orthopedic surgery using the MIMIC-IV database and external validation.Frontiers in surgery · 2026Article
- Early risk stratification and temporal biomarker patterns of trousseau syndrome-related cerebral infarction in lung cancer.Frontiers in oncology · 2026Article
- Left Atrial Appendage Occlusion in Cancer-Associated Atrial Fibrillation: Who, When, and How to Manage Antithrombotic Therapy.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisReview
- Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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12 authors.
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Abstract
Objectives: This study aims to develop cancer-associated venous thromboembolism (CA-VTE) risk prediction models using survival machine learning (ML) algorithms. Methods: This study employed a double-cohort study design (retrospective and prospective). The retrospective cohort ( Results: Univariate analysis and LASSO-COX regression both selected five predictors: age, previous VTE history, ICU/CCU, CCI, and D-dimer. The seven survival ML models (C-index: 0.709-0.760; Brier Score: 0.212-0.243) all outperformed Khorana Score (C-index: 0.632; Brier Score: 0.260) in external validation set. Among all models, the COX_DD model (COX regression + D-dimer) performed best. However, ML models and Khorana Score predicted CA-VTE risk on Conclusions: In this study, the CA-VTE risk prediction models developed in seven survival ML algorithms outperformed Khorana Score. Combining with D-dimer can improve model performance. Applying the nomogram based on the optimal COX_DD model allows oncology nurse to reassess CA-VTE risk once a week. The prediction models developed using survival ML algorithms in this study may contribute to the dynamic and accurate risk assessment of CA-VTE for cancer survivors.
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