Multiparametric prediction with application to early detection of cardiovascular events

Cardiovascular diseases are a major public health concern, and a cause of considerable morbidity and mortality. As a consequence, the prediction of severe events is of great importance for professionals, since it provides the adequate tools to diagnose. The main goal of this work is the development of algorithms that perform the early detection of critical events, using multi-parametric approaches that combine physiological measurements (e.g. ECG, blood pressure, weight) and other sources of information (e.g. medication). Two main scientific challenges will be addressed: prediction methods and information fusion schemes, based on computational intelligence methodologies. The main hypothesis is that physiological time series with similar progression have prognostic value in future clinical states. The main applications will be the prediction of hypertension or decompensation episodes for heart failure patients. The clinical validation will be carried out in the context of tele-monitoring studies using private dataset (myHeart), and intensive care units using public datasets (mimic II-Physionet). This paper presents the main guidelines of research and some preliminary results on the similarity assessment from heart failure patients.