Abujaber Ahmad A, Albalkhi Ibrahem, Imam Yahia, Yaseen Said, Nashwan Abdulqadir J, Akhtar Naveed, Alkhawaldeh Ibrahim M
Nursing Department, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar.
College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Sci Rep. 2025 May 9;15(1):16242. doi: 10.1038/s41598-025-90944-x.
This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from January 2014 to July 2022. Various predictive factors were considered, such as stroke severity at presentation, patient demographics, laboratory results, admission location, and other clinical features. Random forest, logistic regression, XGboost, support vector machines, and decision trees were trained and evaluated. SHAP analysis was conducted to identify key predictors. The RF model demonstrated superior performance in predicting prognosis, while LR was more effective in predicting in-hospital mortality. The National Institute of Health Stroke Score (NIHSS) and admission location were key predictors. Despite its limitations, this research underscores the importance of advancing stroke registries and emphasizes the necessity for comprehensive external validation of predictive models. Furthermore, it demonstrates the importance of initial stroke severity in influencing patient outcomes and highlights the significance of admission to stroke units in reducing poor outcomes. This may help shape interventions to enhance stroke center capacities and influence strategic policies. This study contributes towards developing more precise predictive models for hemorrhagic stroke outcomes, potentially impacting clinical practice and optimizing resource allocation significantly.
本研究旨在利用机器学习模型和SHapley加性解释（SHAP）分析预测出血性卒中的预后，包括90天预后和住院死亡率。数据收集自2014年1月至2022年7月的国家卒中登记处。考虑了各种预测因素，如就诊时的卒中严重程度、患者人口统计学特征、实验室检查结果、入院地点及其他临床特征。对随机森林、逻辑回归、XGBoost、支持向量机和决策树进行了训练和评估。进行SHAP分析以识别关键预测因素。随机森林模型在预测预后方面表现出卓越性能，而逻辑回归在预测住院死亡率方面更有效。美国国立卫生研究院卒中量表（NIHSS）和入院地点是关键预测因素。尽管存在局限性，但本研究强调了推进卒中登记工作的重要性，并强调了对预测模型进行全面外部验证的必要性。此外，研究还证明了初始卒中严重程度对患者预后的影响，以及卒中单元收治对于减少不良预后的重要性。这可能有助于制定干预措施以增强卒中中心的能力，并影响战略政策。本研究有助于开发更精确的出血性卒中预后预测模型，可能对临床实践产生重大影响并显著优化资源分配。
