Enhancing the clinical relevance of haemorrhage prediction models in trauma  

作　　者：Sankalp Tandle Jared M.Wohlgemut Max E.R.Marsden Erhan Pisirir Evangelia Kyrimi Rebecca S.Stoner William Marsh Zane B.Perkins Nigel R.M.Tai 

机构地区：[1]Centre for Trauma Sciences,Blizard Institute,Queen Mary University of London,London E12AT,UK [2]The Royal London Hospital,Barts Health NHS Trust,London E11FR,UK [3]Academic Department of Military Surgery and Trauma,Research and Clinical Innovation,The Royal Centre for Defence Medicine,Birmingham B152WB,UK [4]Department of Electronic Engineering and Computer Science,Queen Mary University of London,London E14NS,UK

出　　处：《Military Medical Research》2024年第3期467-468,共2页军事医学研究（英文版）

基　　金：JMW,RSS,EP,EK,WM,ZBP,and NRMT have received research funding from a precision trauma care research award from the Combat Casualty Care Research Program of the US Army Medical Research and Materiel Command(DM180044).

摘　　要：We read with interest the recent systematic reviewaArtificial intelligence and machine learning for hemorrhagic trauma careoby Peng et al.[1],which evaluated literature on machine learning(ML)in the management of traumatic haemorrhage.We thank the authors for their contribution to the role of ML in trauma.

关 键 词：TRAUMA INJURY Blood transfusion Massive transfusion PREDICTION Artificial intelligence Machine learning 

分 类 号：R641[医药卫生—外科学]