解放军医学院学报2024，Vol.45Issue(3)：223-229,7.DOI:10.12435/j.issn.2095-5227.2024.003
基于机器学习的创伤伤员检伤分类预测模型构建及验证
Construction and validation of a machine learning-based predictive model for trauma casualty triage
摘要
Abstract
Background On-site triage of lot victims at the scene of the trauma is an essential link in first aid,and it is important to study how to triage casualty more effectively and accurately.Objective To develop and validate a predictive model for trauma casualty triage based on vital signs data and machine learning algorithms.Methods A retrospective analysis of pre-hospital emergency trauma casualty data from 2017 to 2019 in the National Trauma Data Bank(NTDB)was performed using five types of models,including Support Vector Machine(SVM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),eXtreme Gradient Boosting(XGBoost),and Multi-Layer Perceptron(MLP)to develop and validate the predictive model for trauma casualty detection and classification.The results were evaluated using Accuracy,Precision,Recall,F1 Score and AUC(area under the ROC curve),and visualized using the ROC curve.The results of the optimal model were also validated in the trauma database of the Emergency Department of the First Medical Centre of Chinese PLA General Hospital.Results A total of 24 948 records of the injured were collected,including 9 496 cases of mild injuries,9 532 cases of moderate injuries,5 496 cases of serious injuries,and 424 cases of critical injuries.Based on the ISS grading criteria,the ROC curve analysis showed that the GBDT algorithm was the most effective compared to the other four models,with an accuracy of 82.63%,a precision of 68.21%,a recall of 60.92%,an F1 value of 61.91%,and an AUC value of 90.38%.In the validation results in the trauma database of the Emergency Department of the First Medical Centre of Chinese PLA General Hospital,the accuracy reached 83.15%,the precision reached 77.38%,the recall reached 59.89%,the F1 value reached 55.26%,and the AUC value reached 90.38%.Conclusion We have successfully developed and validated a set of machine learning predictive models for triage of injuries,which can be applied to assist decision-making for on-site triage of trauma injuries in the future.关键词
创伤/机器学习/检伤分类/预测模型/急救医学Key words
trauma/machine learning/triage/predictive modelling/emergency medicine分类
临床医学引用本文复制引用
张睿智,罗瑞虹,卢志林,李春平,卢兵,邢家溢,黎檀实..基于机器学习的创伤伤员检伤分类预测模型构建及验证[J].解放军医学院学报,2024,45(3):223-229,7.基金项目
军队课题(20224282077) （20224282077）
