Artificial intelligence in the prognostication and classification of cardiovascular diseases

Cardiovascular-related diseases are a major cause of death worldwide. Stroke and cardiac arrest, are among the most common cardiovascular diseases. Prediction models for predicting the risk of complications and outcome of patients who suffer from a cardiovascular disease have been broadly explored. Given the heterogeneous and complex nature of medical data, most common prognostic models focus on a single type of data. This thesis focuses on the application of machine/deep learning for the field of cardiovascular-related diseases, while taking into account the information available in different data types. We developed and evaluated the accuracy or prediction models using a single type of data and the added value of including information from other data types. We also explored multiple combination strategies, using 2D and 3D image representations, Transfer Learning, autoencoders, ResNets, among others . Finally, we strived to make our models transparent by exploring multiple model visualization tools. We developed models for predicting the occurrence of delayed cerebral ischemia in patients who suffered from hemorrhagic stroke, for predicting functional outcome in patients who suffered from ischemic stroke, for predicting outcome after cardiac arrest, and for identifying patients with the Phospholamban gene mutation. This thesis shows that machine/deep learning can be successfully applied to multiple prediction and classification tasks in the field of cardiovascular diseases and can lead to significant improvements in prognosis accuracy. Moreover, it has been shown that combining multiple types of data can have a significant impact in model performance and lead to the discovery of new insights.