Risk prediction for cardiovascular disease

Cardiovascular disease is the single largest killer in the world, causing heart attacks and strokes. Experimental investigation of underlying principals are key in combating this epidemic, however individual considerations are still completely missing.  

The aim of this work is to develop accurate deep learning neural networks algorithms to identify links between patient characteristics and clinical risk. This will help to identify clinical biomarkers which can be deployed in clinical practice for early risk detection.