Development and validation of a machine learning model for predicting in-hospital recurrent intensive care unit admission in critically ill patients with ischemic stroke based on the MIMIC-Ⅳ database
10.12025/j.issn.1008-6358.2026.20260149
- VernacularTitle:基于MIMIC-Ⅳ数据库的机器学习模型对缺血性脑卒中危重患者院内再次转入重症监护病房的预测价值
- Author:
Di ZHANG
1
;
Yuanyuan LIU
1
;
Jian ZHANG
2
;
Xiangjun HU
3
Author Information
1. Department of Rehabilitation Medicine, Shanghai Geriatric Medical Center, Shanghai 201104, China.
2. Department of Rehabilitation, Zhongshan Hospital, Fudan University, Shanghai Institute of Rehabilitation with Integrated Western and Chinese Traditional Medicine, Shanghai 200032, China.
3. Department of Rehabilitation, Zhongshan Hospital, Fudan University, Shanghai Institute of Rehabilitation with Integrated Western and Chinese Traditional Medicine, Shanghai 200032, China;Shanghai Baoshan District Wusong Central Hospital, Shanghai 201900, China.
- Publication Type:Originalarticle
- Keywords:
ischemic stroke;
machine learning;
random forest;
intensive care unit;
MIMIC-Ⅳ
- From:
Chinese Journal of Clinical Medicine
2026;33(3):461-470
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop and validate a prediction model for in-hospital recurrent intensive care unit (ICU) admission in critically ill patients with ischemic stroke (IS) based on machine learning (ML) algorithms. Methods Clinical data from 2 929 IS patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify predictive factors, and the synthetic minority over-sampling technique (SMOTE) was employed to create a derivation cohort comprising 2 583 patients. These patients were randomly divided into a training set (n=2 066) and a test set (n=517) at an 8:2 ratio. Five ML algorithms, including decision tree, random forest, adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and support vector machine (SVM), were performed to construct prediction models. Five-fold cross-validation was used to evaluate the performance of the model in the training set. The area under the receiver operating characteristic curve (ROC-AUC) and decision curve analysis (DCA) were used to assess and compare the models in the testing set. The best-performing model was interpreted by shapley additive explanations (SHAP). Results Among the 2 929 patients included, 704 (24.0%) experienced in-hospital recurrent ICU admission. Among the five ML models, the random forest model demonstrated the best predictive performance, with an AUC of 0.839 (95%CI 0.801–0.877). Feature importance analysis identified five most significant features affecting model prediction, including APS Ⅲ score, albumin, age, heart rate, and SOFA score. Conclusions ML-based models can effectively predict the risk of in-hospital recurrent ICU admission in critically ill patients with IS. The random forest model showed superior predictive performance, which may have potential applications in early clinical risk stratification and intervention.
