Survival Transformers for Longitudinal Data Analysis: Application to Atrial Fibrillation Risk from ECG

Atrial fibrillation (AFib) is the most common sustained cardiac arrhythmia and a major risk factor for stroke, yet it often remains undetected until a first clinical event. Existing deep learning approaches typically estimate AFib risk from a single, cross-sectional ECG, discarding the longitudinal information contained in serial recordings. We propose a transformer-based survival model, SurviFormer, that leverages sequences of ECG representations to model patient-specific AFib risk over time. Our approach encodes irregular inter-visit time gaps as pairwise relative attention biases within a causally masked transformer, and outputs a discrete hazard function trained with a multi-landmark strategy. We train and evaluate our model on the CODE dataset (140k patients, 517k ECGs), with external validation on MIMIC-IV. Compared to static baselines (DeepSurv and DeepHit applied to ResNet-50 embeddings) and a dynamic competing-risk model (DynamicDeepHit), our model achieves a C-index of 0.853, a mean TD-AUC of 0.867, and an IBS of 0.030 on the internal test set, and a C-index of 0.805, a mean TD-AUC of 0.848, and an IBS of 0.106 on the external cohort, demonstrating that longitudinal ECG trajectories provide prognostic value beyond a single recording.