Machine Learning Models Enhance Stroke Risk Prediction in Atrial Fibrillation
Researchers have developed interpretable machine learning models that outperform traditional scoring systems in predicting one-year stroke risk for patients with atrial fibrillation (AF). Published in Nature, the study uses routine clinical data, such as age, comorbidities, and medication history, to provide accurate, personalized risk assessments. These models, including logistic regression and XGBoost, significantly outperform the standard CHA2DS2-VASc score, offering better guidance for anticoagulant therapy decisions. The study involved data from the National Taiwan University Hospital and validated the models across multiple cohorts, demonstrating their robust predictive capabilities. 
