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

ABSTRACT:

Background

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.

Methods

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 performan

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC11918378 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature