医疗卫生装备2025，Vol.46Issue(2)：1-9,9.DOI:10.19745/j.1003-8868.2025021
基于集成学习的急性呼吸窘迫综合征患者疾病严重程度辨识算法研究
Identification algorithm of disease severity in patients with acute respiratory distress syndrome based on ensemble learning
摘要
Abstract
Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]关键词
急性呼吸窘迫综合征/集成学习/疾病严重程度/疾病严重程度辨识/机器学习Key words
acute respiratory distress syndrome/ensemble learning/disease severity/identification of disease severity/machine learning分类
医药卫生引用本文复制引用
杨鹏程,邵新,王春晨,鲍琨,张阳,杜世宸,徐海峰..基于集成学习的急性呼吸窘迫综合征患者疾病严重程度辨识算法研究[J].医疗卫生装备,2025,46(2):1-9,9.基金项目
军队科研项目(ZLJ23J034,22QN059) （ZLJ23J034,22QN059）
新疆维吾尔自治区"天山英才"培养计划优秀工程师项目 （）
