ABSTRACT
Heart failure (HF), the final stage of most heart diseases, is one of the most prevalent pathologies in developed countries, with an exponentially increasing incidence in relation to the aging of the population. Prevention and early diagnosis are two of the most important pillars in strategies to prevent its development and improve its prognosis. The precise study of risk factors and their contribution to HF is essential for good prevention. Through the use of artificial neural networks, important advances have been made in this area. The in-depth analysis of the information from the electrocardiograms using machine learning methods is another of the key points for early diagnosis, essential to quickly start the treatment that allows improving the prognosis. Regarding early care, it is very helpful to have a support system for clinical diagnosis that provides rapid information on the etiology and severity of HF, allowing the development of high-value prognostic scores. In addition, artificial intelligence systems capable of comparing the clinical evolution in the follow-up of patients allow determining the best diagnostic-therapeutic strategy in these patients in a personalized way. Undoubtedly, the technological development of biometric surface contact sensors, capable of obtaining and integrating the information of different parameters, also has its applicability to patients with HF. This technology, together with the development of new internet of things devices and 5G communications technology, will allow us to reach levels of prevention and treatment that were unimaginable up to now in the management of patients with HF.
