Artificial intelligence and machine learning for hemorrhagic trauma care

作     者：Henry T.Peng MMusaab Siddiqui Shawn G.Rhind Jing Zhang Luis Teodoro da Luz Andrew Beckett Henry T.Peng;M.Musaab Siddiqui;Shawn G.Rhind;Jing Zhang;Luis Teodoro da Luz;Andrew Beckett

作者机构：Defence Research and Development CanadaToronto Research CentreTorontoON M3K 2C9Canada Sunnybrook Health Sciences CentreTorontoON M4N 3M5Canada St.Michael’s HospitalTorontoON M5B 1W8Canada Royal Canadian Medical ServicesOttawa K1A 0K2Canada 

出 版 物：《Military Medical Research》 (军事医学研究（英文版）)

年 卷 期：2023年第10卷第5期

页      面：680-698页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：Defence Research and Development Canada Program Activity PEOPLE_014 

主　　题：Artificial intelligence Hemorrhage Machine learning Trauma Injury 

摘      要：Artificial intelligence(AI),a branch of machine learning(ML)has been increasingly employed in the research of trauma in various *** is the most common cause of trauma-related *** better elucidate the current role of AI and contribute to future development of ML in trauma care,we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage.A literature search was carried out on PubMed and Google *** and abstracts were screened and,if deemed appropriate,the full articles were *** included 89 studies in the *** studies could be grouped into five areas:(1)prediction of outcomes;(2)risk assessment and injury severity for triage;(3)prediction of transfusions;(4)detection of hemorrhage;and(5)prediction of *** analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML ***,most studies were retrospective,focused on prediction of mortality,and development of patient outcome scoring *** studies performed model assessment via test datasets obtained from different *** models for transfusions and coagulopathy have been developed,but none is in widespread ***-enabled ML-driven technology is becoming integral part of the whole course of trauma *** and application of ML algorithms using different datasets from initial training,testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.