基于数据驱动的重症监护病房脱机拔管预测模型的研究  

Research on data-driven predictive model for extubation in intensive care unit

作　　者：李松倍 冉宏波 贺宏丽 米金华 杨陈 沈俊 杨皓巍 卢森 兰蕴平[2] 宋章伟 潘纯 黄晓波[2] LI Song-bei;RAN Hong-bo;HE Hong-li;MI Jin-hua;YANG Chen;SHEN Jun;YANG Hao-wei;LU sen;LAN Yun-ping;SONG Zhang-wei;PAN Chun;HUANG Xiao-bo(School of Medical and Life Sciences,Chengdu University of Traditional Chinese Medicine,Chengdu,611137,China;Department of Critical Care Medicine,Sichuan Academy of Medical Sciences·Sichuan Provincial People’s Hospital(Affiliated Hospital of University of Electronic Science and Technology of China),Chengdu 610072,China;School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Department of Critical Care Medicine,Sichuan Provincial People's Hospital Pujiang Hospital/Pujiang County People's Hospital,Chengdu 611630,China;School of Clinical Medicine,Qinghai University,Xining 810000,China;School of Medicine,University of Electronic Science and Technology of China,Chengdu 610054,China;Shanghai Shumu Medical Technology Co.,Ltd.,Shanghai 200232,China)

机构地区：[1]成都中医药大学医学与生命科学学院,四川成都611137 [2]四川省医学科学院·四川省人民医院(电子科技大学附属医院)重症医学科,四川成都610072 [3]电子科技大学自动化工程学院,四川成都611731 [4]四川省人民医院蒲江医院·蒲江县人民医院重症医学科,四川成都611630 [5]青海大学临床医学院,青海西宁810000 [6]电子科技大学医学院,四川成都610054 [7]上海术木医疗科技有限公司,上海200232

出　　处：《实用医院临床杂志》2024年第4期57-62,共6页Practical Journal of Clinical Medicine

基　　金：四川省卫生健康委员会课题(编号:21PJ082);四川省科技厅重点研发项目(编号:2022YFS0605)。

摘　　要：目的 开发并评估一种基于数据驱动的重症监护病房脱机拔管预测模型。方法 收集重症监护病房有创机械通气患者的非时序数据、低频生命体征时序数据和高频机械通气时序数据,采用决策树、朴素贝叶斯、支持向量机、分类集成、广义线性模型和神经网络等算法构建预测模型,并进行性能评估。结果 共纳入204例患者,其中脱机成功患者122例,失败患者82例。含有163例高频机械通气时序数据的深度神经网络模型在预测脱机拔管成功率方面表现最佳,准确度94.2%,AUC值0.81,灵敏度100%,特异性80%。其他模型表现相对逊色,分类集成模型的准确率75%,AUC值0.76,灵敏度83.3%,特异性62.5%。结论 利用远程高频时序数据建立的深度神经网络模型在预测重症监护病房患者脱机拔管成功率上展现出卓越性能,明显超越未采用此类数据的模型。Objective To develop and evaluate a data-driven prediction model for extubation in intensive care unit(ICU).Methods Data was collected from ICU patients undergoing invasive mechanical ventilation.The data included non-sequential data,real-time monitored low-frequency vital signs time-series data,and high-frequency mechanical ventilation time-series data.Prediction models were constructed using algorithms such as decision trees,naive Bayes,support vector machines,ensemble classification,generalized linear models,and neural networks.The performance of these models was then evaluated.Results A total of 204 patients were included,in which 122 patients were successfully extubated and 82 failed extubation.High-frequency mechanical ventilation timeseries data was available for 163 patients,which was used to construct a deep neural network model.This model demonstrated the best performance in predicting extubation success.The accuracy was 94.2%.The area under ROC curve(AUC)was 0.81.The sensitivity was 100%,and the specificity was 80%.Other models showed comparatively inferior performance.The accuracy of the classification ensemble model was 75%,the AUC value was 0.76,the sensitivity was 83.3%,and the specificity was 62.5%.Conclusions This study reveals that the deep neural network model,built with high-frequency time-series data,exhibits outstanding performance in predicting extubation success in ICU patients.It significantly surpasses other models without such data.

关 键 词：机械通气 脱机 机器学习 神经网络 深度学习 时序数据 远程监测 

分 类 号：R56[医药卫生—呼吸系统]