Enhancing the clinical relevance of haemorrhage prediction models in trauma

Military Medical Research

Commentary

Max E. R. Marsden¹  Zane B. Perkins²  Jared M. Wohlgemut²  Rebecca S. Stoner²  Sankalp Tandle²  Nigel R. M. Tai³  Erhan Pisirir⁴  William Marsh⁴  Evangelia Kyrimi⁴ 

[1] Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, E1 2AT, London, UK;Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine, B15 2WB, Birmingham, UK;Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, E1 2AT, London, UK;The Royal London Hospital, Barts Health NHS Trust, E1 1FR, London, UK;Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, E1 2AT, London, UK;The Royal London Hospital, Barts Health NHS Trust, E1 1FR, London, UK;Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine, B15 2WB, Birmingham, UK;Department of Electronic Engineering and Computer Science, Queen Mary University of London, E1 4NS, London, UK;

关键词: Trauma;    Injury;    Blood transfusion;    Massive transfusion;    Prediction;    Artificial intelligence;    Machine learning;   

DOI  :  10.1186/s40779-023-00476-6

 received in 2023-04-22, accepted in 2023-08-22,  发布年份 2023

来源: Springer

【 授权许可】

CC BY   
© People´s Military Medical Press 2023