Deep Learning Survival Model to Predict Atrial Fibrillation From ECGs and EHR Data

Atrial fibrillation (AF) is frequently asymptomatic and at the same time a relevant risk factor for stroke and heart failure. Thus, the identification of patients at high risk of future development of AF from rapid and low-cost exams such as the electrocardiogram (ECG) is of great interest. In this work we trained a deep learning model to predict the risk to develop AF from ECG signals and electronic health records (EHR) data, integrating time-to-event in the model and accounting for death as a competing risk. We showed that our model outperforms the CHARGE-AF clinical risk score and we verified that training the model with both ECGs and EHR data led to better performances with respect to training on single modalities. Models were evaluated both in terms of discrimination and calibration.