实用临床医药杂志2025，Vol.29Issue(8)：28-34,7.DOI:10.7619/jcmp.20245918
基于可解释机器学习构建急性缺血性脑卒中静脉溶栓预后模型
Construction of prognostic model for intravenous thrombolysis in acute ischemic stroke based on interpretable machine learning
摘要
Abstract
Objective To construct machine learning(ML)model for predicting early neurologi-cal deterioration(END)after intravenous thrombolysis(IVT)in patients with acute ischemic stroke(AIS),and to analyze risk factors of END using Shapley additive explanations(SHAP).Methods A total of 97 AIS patients who received IVT were enrolled.Patients were divided into END group(18 cases)and non-END group(79 cases)based on whether they experienced END within 24 hours post-IVT.All patients were randomly divided into training set(n=68)and validation set(n=29)at ra-tio of 7 to 3.Univariate and least absolute shrinkage and selection operator(LASSO)analyses were performed to screen important feature variables associated with END from clinical data.Six ML algo-rithms,including random forest,light gradient boosting machine,decision tree,support vector ma-chine,k-nearest neighbors and extreme gradient boosting,were employed to construct predictive mod-els.Receiver operating characteristic(ROC)curves,calibration curves and clinical decision curve analysis(DC A)were used to evaluate the performance of each ML model.The SHAP method was introduced to interpret the optimal ML model.Results Among the six ML algorithm models,the random forest model was identified as best predictive model.In the training set,it achieved area un-der the curve(AUC)of 0.909,with specificity,precision,recall and F1 score being 0.873,0.856,0.910 and 0.825,respectively.In the validation set,its AUC was 0.915,with correspond-ing values of 0.824,0.800,0.945 and 0.834.Calibration curves and DC A demonstrated that the random forest model had higher prediction accuracy and clinical net benefit.SHAP variable impor-tance plots revealed that the top six contributing imaging factors to END were large-area cerebral in-farction,pre-thrombolysis National Institutes of Health Stroke Scale(NIHSS)score,door-to-needle time(DNT),history of atrial fibrillation,white blood cell(WBC)levels and history of diabetes.Conclusion ML models can effectively predict the risk of END in IVT patients,with the random forest model demonstrating the best predictive performance.Combining SHAP for model visualization interpretation aids clinicians in understanding the contribution of each feature variable to the predic-tion results,thereby facilitating targeted preventive treatment strategies.关键词
机器学习/沙普利加和解释/急性缺血性脑卒中/静脉溶栓/早期神经功能恶化/随机森林模型/大面积脑梗死/美国国立卫生研究院卒中量表Key words
machine learning/shapley additive explanations/acute ischemic stroke/intra-venous thrombolysis/early neurological deterioration/random forest model/large-area cerebral in-farction/National Institutes of Health Stroke Scale分类
临床医学引用本文复制引用
李娟,祁冬,庄雷,司峥..基于可解释机器学习构建急性缺血性脑卒中静脉溶栓预后模型[J].实用临床医药杂志,2025,29(8):28-34,7.基金项目
数字医学与智慧健康安徽省重点实验室(蚌埠医科大学)开放课题基金资助项目(AHCM2024W010) （蚌埠医科大学）
蚌埠市科技创新指导类项目(20220104) （20220104）
