Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

Journal：BMC Medical Research Methodology

Publication type: Article

IF(IF): 4

PMID: 39256656 Link To Pubmed Full text Download

背景：不同类型的急性缺血性卒中的预后、复发率和二级预防策略不同，准确预测入院后急性缺血性卒中的病因分型尤为重要。目前，在卒中领域应用机器学习算法预测病因分型的研究极少。机器学习模型可以捕获医疗数据中复杂的非线性关系，并学习其特征来对它们所描述的实体进行分类，这可能适合于模拟病因学评估的过程，故而对已有TOAST/CCS分型进行补充。 目的：本研究旨在利用机器学习和深度学习算法来识别急性缺血性卒中患者病因，并测试模型的区分度和校准度，找到最优模型和最优特征集。对已有病因分型进行补充，建立可以辅助诊断卒中病因工具，帮助临床医生快速对急性缺血性卒中患者进行病因分型。

Methods This study involved patients with the Third China National Stroke Registry (CNSR-III). Nine models, which included Natural Gradient Boosting (NGBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LGBM), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and logistic regression (LR), were employed to predict large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE) using an 80:20 randomly split training and test set. We designed an SFS-XGB with 10-fold cross-validation for feature selection. The primary evaluation metrics for the models included the area under the receiver operating characteristic curve (AUC) for discrimination and the Brier score (or calibration plots) for calibration.