This thesis explores Deep Learning systems in advanced heart failure and Left Ventricular Assist Device (LVAD) candidacy assessment. We developed deep learning classification and semantic segmentation for 12-lead Electrocardiograms (ECG) from multiple datasets, proposed criteria for candidacy, and implemented interpretability with saliency maps and uncertainty awareness, to aid in LVAD candidate selection. Furthermore, we analyze long-term LVAD patient ECG pre-implantation progressions using an external test set. In a second aspect, we link hemodynamic response to inotropes with VAD-related outcomes by analyzing physiological time series using an ensemble approach for multi-modal waveforms in the pediatric cardiac intensive care setting of a Houston-based hospital. Our system processes minute-by-minute data with the aim of identifying the need for mechanical circulatory support. The predictions help towards early identification of high-risk patients after only two days from admission, and the estimation of feature importance confirms the predictive ability of the hemodynamic early response to inotropes.