Comparison of the in-hospital mortality risk predictive models among patients with ischemic stroke combined by dysphagia based on interpretable machine learning
10.19428/j.cnki.sjpm.2025.24973
- VernacularTitle:基于可解释机器学习的缺血性脑卒中合并吞咽困难患者院内死亡风险预测模型比较
- Author:
Yaoyong TAI
1
;
Shengyong WU
1
;
Xiao LUO
1
;
Ronghui ZHU
1
;
Qian HE
1
;
Cheng WU
1
Author Information
1. Faculty of Health Services, Naval Medical University, Shanghai 200433, China
- Publication Type:Journal Article
- Keywords:
interpretable machine learning;
ischemic stroke;
dysphagia;
in-hospital mortality;
risk prediction
- From:
Shanghai Journal of Preventive Medicine
2025;37(3):199-205
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo predict the in-hospital mortality risk among patients with ischemic stroke combined by dysphagia using interpretable machine learning methods, so as to provide more evidence-based support for the prognosis prediction of patients with ischemic stroke combined by dysphagia. MethodsMedical record of 308 patients diagnosed with ischemic stroke combined by dysphagia in the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) (2.0) in the United States were retrospectively analyzed. Features of the research data were screened based on the least absolute shrinkage and selection operator, and which were randomly divided into a training set and a test set at a ratio of 7∶3. Then ten models, including logistic regression, random forest, K-nearest neighbor, linear discriminant analysis, naive bayes (NB), neural network, quadratic discriminant analysis, recursive partitioning tree, extreme gradient boosting tree, and support vector machine, etc. were constructed. The predictive effect was measured by calculating the area under the curve (AUC) of receiver operating characteristics. In addition, the calibration curve and Brier score were used to evaluate the calibration degree of the model, and the decision curve was drawn to reflect the clinical net benefit. The Shapley additive explanation method was used to analyze the interpretability of the black box model and explore the important decision-making factors. ResultsThe NB model in the test set showed better predictive ability compared with other models (AUC=0.85, 95%CI: 0.83‒0.88). After interpretability analysis, it was found that blood urea nitrogen (BUN), age, sequential organ failure assessment, bicarbonate, chloride, and hypertension were important risk factors for in-hospital mortality in patients with ischemic stroke combined by dysphagia. ConclusionThe comprehensive performance of the NB model is better than that of the other nine models in predicting the risk of in-hospital mortality in patients with ischemic stroke combined by dysphagia. The interpretability of the model can help clinicians better understand the reasons behind the results and take further reasonable intervention measures for risk factors to improve the survival probability of patients.
