Leveraging Advanced Preprocessing and CNN for Mortality and Rehospitalization Prediction in Heart Failure Patients

Heart failure (HF) is an important global health problem with high rates of morbidity and mortality and frequent rehospitalizations. Such adverse outcomes can be predicted timely so as to improve clinical decision making and also to optimise patient management strategies. Based on our findings in this work, we propose a new methodology to leverage Convolutional Neural Networks (CNNs) that analyse both medical imaging data, along with structured clinical records towards predicting mortality and rehospitalization risk in patients with HF. We employ advanced preprocessing techniques to convert time-series vital signs and echocardiographic images into CNN-compatible formats. We train the model on a massive dataset composed of historical patient records to discover complicated patterns of increased risk. Finally, we show that the CNN model achieves strong predictive accuracy, exceeding other existing machine learning methods. We also use visualisation techniques, to interpret and analyse the model some of the main features that have an effect on the model predictions. This research points to the feasibility of using Deep Learning techniques in clinical practise for proactive intervention and improved outcomes in patients with heart failure.