Big and deep hype and hope: On the special issue for deep learning and big data in healthcare

Deep Learning networks are revolutionizing both the academic and the industrial scenarios of information and communication technologies. Their theoretical maturity and the coexistence of large datasets with computational media is making this technology available to a wide community of makers and users, and recent evolution has been remarkable in techniques such as deep belief networks, Boltzmann machines, auto encoders, or recurrent networks. In a different yet often closely related arena, the analysis of large amounts of data from the Electronic Health Recording, the Hospital Information Systems, and other medical data sources, success cases on companies, and new products have made possible new tools for estimation of in-hospital stay duration, chronic patient identification, and policies to reduce readmissions by preventing illness progression. Large and small companies have paid attention to this new era, in which machine learning and statistical analysis need to be revisited if they want to provide suitable algorithms, especially in healthcare scenarios, where patient data more than ever is becoming the key to improving patient healthcare.
Healthcare is now an open field for advantageous use of deep learning and big data advancements, and challenges are open in order to provide systems that can be accurate enough to be useful to the clinician and the patient in the health itinerary. Not only are large amounts of data available, but also sensitivity and specificity are to be paid special attention, as well as support systems rationally fitting into the health system.