基于机器学习算法的急诊创伤患者用血预测模型的构建  

Construction of blood consumption prediction model for emergency trauma patients based on machine learning algorithm

作　　者：潘凌凌[1,2] 胡哲畅 李强 韩文娟[1,2] 胡伟 Pan Lingling;Hu Zhechang;Li Qiang;Han Wenjuan;Hu Wei(Blood Center of Zhejiang Province,Hangzhou 310052,China;Key Laboratory of Blood Safety Research of Zhejiang Province,Hangzhou 310052,China;Emergency Department of the second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310052,China)

机构地区：[1]浙江省血液中心,杭州310052 [2]浙江省血液安全研究重点实验室,杭州310052 [3]浙江大学医学院附属第二医院急诊科,杭州310052

出　　处：《中华急诊医学杂志》2023年第5期606-611,共6页Chinese Journal of Emergency Medicine

基　　金：浙江省基础公益研究计划(LGF20G030002)。

摘　　要：目的建立基于机器学习算法的急诊创伤患者用血预测模型,以指导采供血机构做好突发公共事件下群体伤员早期血液需求准备。方法采用回顾性分析,以2018年1月至2020年12月浙江省12家医院急诊系统数据库中创伤类型患者为研究对象,排除血液病、肿瘤等慢性病史及外院诊治后转入病例。依据是否输血治疗分为输血组和未输血组,比较两组患者在人口学及临床特点等方面的差异,采用计算机学习算法(XGBoost)构建急诊创伤患者用血预测模型和用血量预测模型。结果本研究最终纳入2025例患者资料,其中输血组1146例,未输血组879例。急诊创伤患者用血需求主要发生在入院3 d内(60%)。影响急诊创伤患者用血预测模型的主要变量为休克指数、红细胞压积、收缩压、腹部受伤、骨盆受伤、腹腔积液和血红蛋白。通过机器学习构建出的急诊创伤患者输血预测模型与传统预测模型对比,XGBoost模型命中率最高,达59.0%。构建的用血量预测模型采用七级用血量分级时其准确度最高,偏差在0~1 U之间浮动。根据预测模型得到用血预测公式为Σnw×c。结论初步构建的急诊创伤患者输血预测和用血量预测模型效能优于传统输血预测模型,为突发公共事件下,医院与采供血机构优化血液需求评估提供参考。Objective To establish a blood consumption prediction model for emergency trauma patients based on machine learning algorithm,so as to guide blood collection and blood supply institutions to prepare for the early blood demand of mass casualties in public emergencies.Methods A retrospective analysis was conducted on trauma patients in the emergency system database of 12 hospitals in Zhejiang Province from January 2018 to December 2020.Patients with chronic medical history such as hematological diseases and tumors,and transferred from other hospitals after external treatment were excluded.The patients were divided into the transfusion group and non-transfusion group according to whether they received blood transfusion.The differences in demographic and clinical characteristics between the two groups were compared,and the computer learning algorithm(XGBoost)was used to build the blood consumption prediction model and blood consumption volume prediction model of emergency trauma patients.Results Totally 2025 patients were included in this study,including 1146 patients in the transfusion group and 879 patients in the non-transfusion group.The blood demand of emergency trauma patients mainly occurred within 3 days of admission(60%).The main variables affecting the blood consumption prediction model of emergency trauma patients were shock index,hematocrit,systolic blood pressure,abdominal injury,pelvic injury,ascites and hemoglobin.Compared with the traditional prediction model,XGBoost model had the highest hit rate of 59.0%.The accuracy of blood consumption prediction model was the highest when seven levels of blood volume were adopted,and the deviation fluctuated between[0~1]U.According to the prediction model,the blood consumption prediction formula wasΣnw×c.Conclusions The preliminarily constructed prediction model of blood transfusion and blood consumption for emergency trauma patients has better performance than the traditional prediction model of blood transfusion,which provides reference for optimizing the dec

关 键 词：公共事件 用血预测模型 机器学习算法 急诊创伤 血液保障 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R641[自动化与计算机技术—控制科学与工程]