Enhancing the clinical relevance of haemorrhage prediction models in trauma

Mil Med Res. 2023 Sep 20;10(1):43. doi: 10.1186/s40779-023-00476-6.

Sankalp Tandle^{1

2}, Jared M Wohlgemut^{3

4}, Max E R Marsden^{3

5}, Erhan Pisirir⁶, Evangelia Kyrimi⁶, Rebecca S Stoner^{3

4}, William Marsh⁶, Zane B Perkins^{3

4}, Nigel R M Tai^{3

4

5}

¹ Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, E1 2AT, UK. sankalp.tandle1@nhs.net.
² The Royal London Hospital, Barts Health NHS Trust, London, E1 1FR, UK. sankalp.tandle1@nhs.net.
³ Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, E1 2AT, UK.
⁴ The Royal London Hospital, Barts Health NHS Trust, London, E1 1FR, UK.
⁵ Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine, Birmingham, B15 2WB, UK.
⁶ Department of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.

No abstract available

Keywords: Artificial intelligence; Blood transfusion; Injury; Machine learning; Massive transfusion; Prediction; Trauma.

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