Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

Deep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.