Journal Article

Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care

Li, Rong, Li, Ao, Li, Guisu, Li, Yanfeng, Li, Zhihuan, Zhao, Qingshi, Jiang, Youli

2025

Overview

Venous thromboembolism (VTE) is a life-threatening complication commonly occurring after acute ischemic stroke (AIS), with an increased risk of mortality. Traditional risk assessment tools lack precision in predicting VTE in AIS patients due to the omission of stroke-specific factors. We developed a machine learning model using clinical data from patients with acute ischemic stroke (AIS) admitted between December 2021 and December 2023. Predictive models were developed using machine learning algorithms, including Gradient Boosting Machine (GBM), Random Forest (RF), and Logistic Regression (LR). Feature selection involved stepwise logistic regression and LASSO, with SHapley Additive exPlanations (SHAP) used to enhance model interpretability. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Among the 1,632 AIS patients analyzed, 4.17% developed VTE. The GBM model achieved the highest predictive accuracy with an AUC of 0.923, outperforming other models such as Random Forest and Logistic Regression. The model demonstrated strong sensitivity (90.83%) and specificity (93.83%) in identifying high-risk patients. SHAP analysis revealed that key predictors of VTE risk included elevated D-dimer levels, premorbid mRS, and large vessel occlusion, offering clinicians valuable insights for personalized treatment decisions. This study provides an accurate and interpretable method to predict VTE risk in patients with AIS using the GBM model, potentially improving early detection rates and reducing morbidity. Further validation is needed to assess its broader clinical applicability.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Aged / Algorithms / Brain cancer / Cardiac arrhythmia / Care and treatment / China / Complications and side effects / Computer and Information Sciences / Data analysis / Data mining / Data processing / Datasets / Decision trees / Diabetes / Feature selection / Female / Forecasts and trends / Health aspects / Health risk assessment / Hospitals / Humans / Ischemia / Ischemic Stroke - complications / Learning algorithms / Logistic Models / Machine Learning / Male / Medical imaging / Medical research / Medicine and Health Sciences / Medicine, Experimental / Methods / Middle Aged / Morbidity / Mortality / Mortality risk / Occlusion / Patients / Physical Sciences / Prediction models / Prevention / Regression analysis / Regression models / Research and Analysis Methods / Risk assessment / Risk Assessment - methods / Risk Factors / Risk groups / ROC Curve / Statistical analysis / Stroke / Stroke (Disease) / Thromboembolism / Thrombosis / Ultrasonic imaging / Venous Thromboembolism - diagnosis / Venous Thromboembolism - epidemiology / Venous Thromboembolism - etiology