Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
Researchers have developed an interpretable machine learning model, named Pre-AF 13, to predict the risk of atrial fibrillation (AF) in cardiovascular disease patients. The model, trained on electronic health records from Russia, uses natural language processing to extract features from discharge reports. Pre-AF 13 demonstrated superior performance compared to existing clinical risk scores, achieving an ROC AUC of 0.725 for 24-month prediction. AI
IMPACT This research demonstrates the potential for interpretable ML models to improve diagnostic accuracy in healthcare, potentially leading to earlier interventions.