Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients
The SVC model delivered the best overall performance outperforming similar studies that developed deep learning and machine-learning models for COVID-19.
Authors
- Mohammad Hossein Amirhosseini (ORCID: https://orcid.org/0000-0002-3404-084X)
- Michael Makris (ORCID: https://orcid.org/0000-0001-7622-7939)
- Indika Rajakaruna
- Michael Laffan (ORCID: https://orcid.org/0000-0002-8268-3268)
- Deepa J. Arachchillage (ORCID: https://orcid.org/0000-0001-5993-4850)
- Yang Li (ORCID: https://orcid.org/0000-0003-4022-7341)
Institutions
- Imperial College Healthcare NHS Trust (GB)
- University of East London (GB)
- Imperial College London (GB)
- University of Sheffield (GB)
Publication Details
- Journal
- Research and Practice in Thrombosis and Haemostasis
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1016/j.rpth.2025.102711
- Citations
- 3
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 5.61
Funders
- Bayer
- Imperial College London
- LEO Pharma
- Medical Research Council
- NIHR Imperial Biomedical Research Centre