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Contemporary research in cardiovascular medicine is aimed at both the understanding of the fundamental properties of the cardiovascular system and its changes with disease, as well as providing evidence-based guidelines towards best clinical practice. Linking basic understanding with clinical practice is the objective of translational biomedicine, in which "reality-driven" research is key, underlining the concept that direct human observations are essential for the study of hypotheses relevant to human reality [Mankoff S, Journal of Translational Medicine 2004]. However, reality-driven biomedical research is accompanied by important hurdles since interpreting complex observations and linking these to relevant basic and clinical understanding of the human and their diseases require an interaction of basic scientists from all disciplines, technologists and computer scientists and clinical specialists, all speaking different languages. Here, the use of computational techniques, being it physiological modelling or contemporary machine learning-based data analysis, can assist in understanding clinical problems and offer valuable insights.