>
网站首页期刊介绍通知公告编 委 会投稿须知电子期刊广告合作联系我们
最新消息:
人工智能在创伤性出血救治中的应用及其研究进展
作者:李广科  王根旺  卓兴峰  胡玮  马龙  李向阳  朱道俊 
单位:东部战区总医院, 江苏 南京 210002
关键词:人工智能 机器学习 创伤分类 临床决策 综述 
分类号:R605.972
出版年·卷·期(页码):2024·43·第四期(628-634)
摘要:

人工智能在创伤性出血救治中的应用和研究取得了显著进展。为深入了解人工智能在创伤救治中的作用,本文作者对其在创伤性出血的诊断和治疗策略中的应用进行了文献综述,旨在比较不同AI算法在处理创伤性出血患者数据时的表现,选择最适合临床决策支持的算法从而提高其在实际临床应用中的准确性和有效性。

参考文献:

[1] BICKELL W H,WALL M J,PEPE P E,et al.Immediate versus delayed fluid resuscitation for hypotensive patients with penetrating torso injuries[J].N Engl J Med,1994,331(17):1105-1109.
[2] KAUVAR D S,WADE C E.The epidemiology and modern management of traumatic hemorrhage:US and international perspectives[J].Crit Care,2005,9(Suppl 5):1-9.
[3] KAUVAR D S,LEFERING R,WADE C E.Impact of hemorrhage on trauma outcome:an overview of epidemiology,clinical presentations,and therapeutic considerations[J].J Trauma,2006,60(6 Suppl):S3-S9.
[4] KATZENELL U,ASH N,TAPIA A L,et al.Analysis of the causes of death of casualties in field military setting[J].Mil Med,2012,177(9):1065-1068.
[5] WOOLLEY T,GWYTHER R,PARMAR K,et al.A prospective observational study of acute traumatic coagulopathy in traumatic bleeding from the battlefield[J].Transfusion,2020,60(Suppl3):S52-S61.
[6] UDDIN M,WANG Y,WOODBURY-SMITH M.Artificial intelligence for precision medicine in neurodevelopmental disorders[J].NPJ Digit Med,2019,2:112.
[7] KUMAR Y,GUPTA S,SINGLA R,et al.A systematic review of artificial intel-ligence techniques in cancer prediction and diagnosis[J].Arch Computat Methods Eng,2022,29(4):2043-2070.
[8] PAUL D,SANAP G,SHENOY S,et al.Artificial intelligence in drug discovery and development[J].Drug Discov Today,2021,26(1):80-93.
[9] TRAN Z,ZHANG W,VERMA A,et al.The derivation of an international classification of diseases,tenth revision-based trauma-related mortality model using machine learning[J].Trauma Acute Care Surg,2022,92(3):561-566.
[10] SEFRIOUI I,AMADINI R,MAURO J,et al.Survival prediction of trauma patients:a study on US national trauma data bank[J].Eur Trauma Emerg Surg,2017,43(6):805-822.
[11] KIM D,YOU S,SO S,et al.A data-driven artificial intelligence model for remote triage in the prehospital environment[J].PLoS One,2018,13(10):e0206006.
[12] AHMED F S,ALI L,JOSEPH B A,et al.Astatistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit[J].J Trauma Acute Care Surg,2020,89(4):736-742.
[13] BECALICK D C,COATS T J.Comparison of artificial intelligence techniques with UKTRISS for estimating probability of survival after trauma.UK trauma and injury severity score[J].J Trauma,2001,51(1):123-133.
[14] KUO P J,WU S C,CHIEN P C,et al.Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders:a cross-sectional retrospective study in southern Taiwan[J].BMJ Open,2018,8(1):e018252.
[15] MAURER L R,BERTSIMAS D,BOUARDI H T,et al.Trauma outcome predictor:an artificial intelligence interactive smartphone tool to predict outcomes in trauma patients[J].Trauma Acute Care Surg,2021,91(1):93-99.
[16] KILIC Y A,KONAN A,YORGANCI K,et al.A novel fuzzy-logic inference system for predicting trauma-related mortality:emphasis on the impact of response to resuscitation[J].Eur J Trauma Emerg Surg,2010,36(6):543-550.
[17] CARDOSI J D,SHEN H,GRONER J I,et al.Machine learning for outcome predictions of patients with trauma during emergency department care[J].BMJ Health Care Inform,2021,28(1):e100407.
[18] LIU N T,HOLCOMB J B,WADE C E,et al.Utility of vital signs,heart rate variability and complexity,and machine learning for identifying the need for lifesaving interventions in trauma patients[J].Shock,2014,42(2):108-114.
[19] LIU N T,HOLCOMB J B,WADE C E,et al.Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients[J].Med Biol Eng Comput,2014,52(2):193-203.
[20] BATCHINSKY A I,SALINAS J,JONES J A,et al.Predicting the need to perform life-saving interventions in trauma patients by using new vital signs and artificial neural networks[C]//COMBI C,SHAHAR Y,ABU-HANNA A.Conference on artificial intelligence in medicine in Europe.Berlin:Springer,2009:390-394.
[21] KIM D,CHAE J,OH Y,et al.Automated remote decision-making algorithm as a primary triage system using machine learning techniques[J].Physiol Meas,2021,42(2):025006.
[22] SCERBO M,RADHAKRISHNAN H,COTTON B,et al.Prehospital triage of trauma patients using the random forest computer algorithm[J].J Surg Res,2014,187(2):371-376.
[23] PAYDAR S,PARVA E,GHAHRAMANI Z, et al.Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation?Using data mining artificial intelligence[J].Chin J Traumatol,2021,24(1):48-52.
[24] PENNELL C,POLET C,ARTHUR L G,et al.Risk assessment for intra-abdominal injury following blunt trauma in children:derivation and validation of a machine learning model[J].J Trauma Acute Care Surg,2020,89(1):153-159.
[25] LAMMERS D,MARENCO C,MORTE K,et al.Machine learning for military trauma:novel massive transfusion predictive models in combat zones[J].J Surg Res,2022,270:369-375.
[26] FENG Y N,XU Z H,LIU J T,et al.Intelligent prediction of RBC demand in trauma patients using decision tree methods[J].Mil Med Res,2021,8(1):33.
[27] ZHOU Y,DREIZIN D,LI Y,et al.Multi-scale attentional network for multi-focal segmentation of active bleed after pelvic fractures[C]//SUK H,LIU M,YAN P,et al.International conference on machine learning in medical imaging.Cham:Springer,2019:461-469.
[28] LANG E,NEUSCHWANDER A,FAVÉ G,et al.Clinical decision support for severe trauma patients:machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury[J].J Trauma Acute Care Surg,2022,92(1):135-143.
[29] DAVULURI P,WU J,TANG Y,et al.Hemorrhage detection and segmentation in traumatic pelvic injuries[J].Comput Math Methods Med,2012,2012:898430.
[30] CONVERTINO V A,MOULTON S L,GRUDIC G Z,et al.Use of advanced machine-learning techniques for noninvasive monitoring of hemorrhage[J].J Trauma,2011,71(1 Suppl):25-32.
[31] DAVIS M A,RAO B,CEDENO P A,et al.Machine learning and improved quality metrics in acute intracranial hemorrhage by noncontrast computed tomography[J].Curr Probl Diagn Radiol,2022,51(4):556-561.
[32] HE L,LUO L,HOU X,et al.Predicting venous thromboembolism in hospitalized trauma patients:a combination of the caprini score and data-driven machine learning model[J].BMC Emerg Med,2021,21(1):60.
[33] LI K,WU H,PAN F,et al.A machine learning-based model to predict acute traumatic coagulopathy in trauma patients upon emergency hospitalization[J].Clin Appl Thromb Hemost,2020,26:1076029619897827.
[34] PERKINS Z B,YET B,MARSDEN M,et al.Early identification of trauma-induced coagulopathy:development and validation of a multivariable risk prediction model[J].Ann Surg,2021,274(6):e1119-e1128.
[35] HANLEY J A,McNEIL B J.The meaning and use of the area under a receiver operating characteristic (ROC) curve[J].Radiology,1982,143(1):29-36.
[36] WALCZAK S.Artificial neural network medical decision support tool:predicting transfusion requirements of ER patients[J].IEEE Trans Inf Technol Biomed,2005,9(3):468-474.
[37] LEE K C,LIN T C,CHIANG H F,et al.Predicting outcomes after trauma:prognostic model development based on admission features through machine learning[J].Medicine,2021,100(49):e27753.
[38] TSIKLIDIS E J,SIMS C,SINNO T,et al.Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission[J].PLoS One,2020,15(11):e0242166.
[39] FOLLIN A,JACQMIN S,CHHOR V,et al.Tree-based algorithm for prehospital triage of polytrauma patients[J].Injury,2016,47(7):1555-1561.
[40] LARSSON A,BERG J,GELLERFORS M,et al.The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression:a simulation study[J].BMC Med Inform Decis Mak,2021,21(1):192.
[41] MINA M J,WINKLER A M,DENTE C J.Let technology do the work:improving prediction of massive transfusion with the aid of a smartphone application[J].Trauma Acute Care Surg,2013,75(4):669-675.
[42] CHEN L,McKENNA T M,REISNER A T,et al.Decision tool for the early diagnosis of trauma patient hypovolemia[J].J Biomed Inform,2008,41(3):469-478.
[43] CHESNEY T,PENNY K,OAKLEY P,et al.Data mining medical information:should artificial neural networks be used to analyse trauma audit data?[J].IJHISI,2006,1(2):51-64.
[44] ROVEDA G,KOLEDOYE M A,PARIMBELLI E,et al.Predicting clinical outcomes in patients with traumatic bleeding:a secondary analysis of the CRASH-2 dataset[C]//2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI).Modena:IEEE,2017:1-6.
[45] 窦维佳,康林,刘喜,等.人工智能技术在早期胃癌内镜诊断中的应用进展[J].中国现代医学杂志,2023,33(6):37-42.

服务与反馈:
文章下载】【发表评论】【查看评论】【加入收藏
提示:您还未登录,请登录!点此登录
您是第 489132 位访问者


copyright ©《东南大学学报(医学版)》编辑部
联系电话:025-83272481 83272483
电子邮件:
bjb@pub.seu.edu.cn

本系统由北京博渊星辰网络科技有限公司设计开发 技术支持电话:010-63361626

苏ICP备09058364