Spatiotemporal

We introduce CoRel, a novel distribution-free conformal prediction method that leverages graph neural networks to quantify uncertainty in correlated time series forecasting by exploiting relational dependencies across sequences.

This paper introduces a new method to visualise cardiac fibrillation dynamics termed ‘imputation mapping’. Using a graph neural network approach, global atrial fibrillation dynamics are reconstructed from sparse clinical measurements, achieving superior performance in tracking cardiac arrhythmia patterns compared to existing methods.