Published on in Vol 27 (2025)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/77339, first published
.
Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis
Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis
Journals
- Duranteau O, Leon D. Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients. Current Opinion in Anaesthesiology 2026;39(2):154 View
- Leung P, Ho P, Lim H. Contemporary Challenges in Venous Thromboembolism: Evolving Populations and Implications for Management and Risk Stratification. Journal of Clinical Medicine 2026;15(4):1509 View
- Crisan D, Cut T, Herlo L, Ivanovic N, Herlo A, Alexandrescu L, Sălcudean A, Dumache R. Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing. Journal of Cardiovascular Development and Disease 2026;13(3):119 View
- Cordeanu E, Stephan D. Can machine-learning algorithms replace clinical prediction rules for deep vein thrombosis diagnosis?. Archives of Cardiovascular Diseases 2026 View
- Abdelhamid A, Moustafa H, Nafea H, Abdelhay E, Abo-Zahhad M, El-Ghamry A. Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data. Scientific Reports 2026;16(1) View
- Xia A, Liu J, Song J, Han Y, Ding B, Xie A, Liu C, Tang X, Xing W, Zhou D, Liu L, Zhou H. Development and validation of an interpretable machine learning model for venous thromboembolism risk prediction in patients with lung cancer: a real-world study. Frontiers in Medicine 2026;13 View