SynthesisJournal of medical Internet research2025
Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting lower extremity deep vein thrombosis in elderly patients with hip fracture: A machine learning model with emphasis on diabetes as a key risk factor.Experimental and therapeutic medicine · 2026Article
- Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Development and validation of a predictive model for perioperative venous thromboembolism in colorectal cancer patients based on D-dimer combined with fibrinogen levels.Translational cancer research · 2026Article
- Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.Scientific reports · 2026Article
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Contemporary Challenges in Venous Thromboembolism: Evolving Populations and Implications for Management and Risk Stratification.Journal of clinical medicine · 2026Review
- Construction and verification of a nomogram to predict the probability of venous thromboembolism in lung cancer patients.Frontiers in molecular biosciences · 2026Article
- 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 · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the increasing use of machine learning (ML)-based risk prediction models for venous thromboembolism (VTE) in patients, the quality and applicability of these models in practice and future research remain unknown. The prediction mechanism of ML and the number of selected factors have been research hotspots in VTE prediction. Objective: This study aimed to systematically review the literature on the predictive value of ML for VTE. Methods: PubMed, Web of Science, MEDLINE, Embase, CINAHL, and Cochrane Library databases were searched for studies published up to March 26, 2025. Studies that developed and validated an ML model for VTE prediction in the patient population and were published in English were eligible, and studies with duplicate data were excluded. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias in the included studies. Meta-analyses were performed to evaluate the C-index, sensitivity, and specificity. Results: A total of 27 studies with 596,092 patients reported the assessment value of ML models for predicting VTE. The risk of bias assessment yielded 18 (67%) studies with a high risk of bias, 8 (30%) with an unclear risk of bias, and 1 (4%) with a low risk of bias. The pooled sensitivity and specificity were 0.79 (95% CI 0.78-0.80) and 0.82 (95% CI 0.81-0.82), respectively. The positive likelihood ratio was 5.02 (95% CI 3.81-6.60), the negative likelihood ratio was 0.27 (95% CI 0.22-0.33), and the diagnostic odds ratio was 20.14 (95% CI 13.69-29.63; P<.001). A random-effects model was leveraged for meta-analysis of the C-index, which was 0.84 (95% CI 0.80-0.88). The most significant predictors for VTE were age, D-dimer level, and VTE history. Conclusions: ML has been shown to effectively predict VTE in patients. However, a high risk of bias was identified in most of the included studies (18/27, 67%), primarily due to shortcomings in handling missing data and reporting the study design. Consequently, future research must prioritize external validation and address methodological rigor to facilitate the translation of these models into routine clinical practice.
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