Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting
Early after injury, an ML system performs well compared with expert prehospital clinicians in the prediction of TIC and blood transfusion. The study suggests that ML systems may augment clinical risk prediction in trauma.
Authors
- Max Marsden (ORCID: https://orcid.org/0000-0002-7147-6861)
- Evangelia Kyrimi (ORCID: https://orcid.org/0000-0001-6727-2279)
- Anne Weaver (ORCID: https://orcid.org/0000-0002-8759-6315)
- William Marsh (ORCID: https://orcid.org/0000-0003-0212-6363)
- Andrea Rossetto (ORCID: https://orcid.org/0000-0002-9176-8351)
- Erhan Pişirir (ORCID: https://orcid.org/0000-0002-2365-4643)
- Nigel Tai (ORCID: https://orcid.org/0000-0001-8493-9063)
- Zane Perkins (ORCID: https://orcid.org/0000-0003-4807-8803)
- Ross Davenport (ORCID: https://orcid.org/0000-0002-8593-6582)
- Richard Lyon (ORCID: https://orcid.org/0000-0002-6339-5666)
Institutions
- Queen Mary University of London (GB)
- Barts Health NHS Trust (GB)
- University of Surrey (GB)
- London Ambulance Service NHS Trust (GB)
- Ministry of Defence (SI)
Publication Details
- Journal
- Emergency Medicine Journal
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1136/emermed-2024-214396
- Citations
- 3
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.76