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Semin Thromb Hemost
DOI: 10.1055/a-2669-7933
Review Article

Machine Learning in Venous Thromboembolism – Why and What Next?

Gerard Gurumurthy
1   The University of Manchester, Manchester, United Kingdom
,
Filip Kisiel
2   Photon Science Institute, The University of Manchester, Manchester, United Kingdom
3   Department of Chemical Engineering, The University of Manchester, Manchester, United Kingdom
,
Lianna Reynolds
4   Royal Manchester Children's Hospital, Manchester, United Kingdom
,
Will Thomas
5   Haemophilia Comprehensive Care Centre, Addenbrooke's Hospital, Cambridge, United Kingdom
,
Maha Othman
6   Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada
7   School of Baccalaureate Nursing, St. Lawrence College, Kingston, Ontario, Canada
8   Clinical Pathology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
,
Deepa J. Arachchillage
9   Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, United Kingdom
10   Department of Haematology, Imperial College Healthcare NHS Trust, London, United Kingdom
,
Jecko Thachil
11   Department of Haematology, Manchester University Foundation Trust, Manchester, UK
› Author Affiliations
Funding D.J.A. is funded by the Medical Research Council, United Kingdom (MR/Z505274/1). All other authors declare no funding.
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Abstract

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE arises from diverse and overlapping risk factors, such as inherited thrombophilia, immobility, malignancy, surgery or trauma, pregnancy, hormonal therapy, obesity, chronic medical conditions (e.g., heart failure, inflammatory disease), and advancing age. Clinicians, therefore, face challenges in balancing the benefits of thromboprophylaxis against the bleeding risk. Existing clinical risk scores often exhibit only modest discrimination and calibration across heterogeneous patient populations. Machine learning (ML) has emerged as a promising tool to address these limitations. In imaging, convolutional neural networks and hybrid algorithms can detect VTE on CT pulmonary angiography with areas under the curves (AUCs) of 0.85 to 0.96. In surgical cohorts, gradient-boosting models outperform traditional risk scores, achieving AUCs between 0.70 and 0.80 in predicting postoperative VTE. In cancer-associated venous thrombosis, advanced ML models demonstrate AUCs between 0.68 and 0.82. However, concerns about bias and external validation persist. Bleeding risk prediction models remain challenging in extended anticoagulation settings, often matching conventional models. Predicting recurrent VTE using neural networks showed AUCs of 0.93 to 0.99 in initial studies. However, these lack transparency and prospective validation. Most ML models suffer from limited external validation, “black box” algorithms, and integration hurdles within clinical workflows. Future efforts should focus on standardized reporting (e.g., Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis [TRIPOD]-ML), transparent model interpretation, prospective impact assessments, and seamless incorporation into electronic health records to realize the full potential of ML in VTE.

Keywords

venous thromboembolism - artificial intelligence - machine learning

Supplementary Material



Publication History

Received: 15 July 2025

Accepted: 28 July 2025

Article published online:
19 August 2025

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