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Special Issue "Deep Learning and Big Data in Healthcare"
Deadline for manuscript submissions: closed (20 April 2019).
Prof. Dr. José Luis Rojo-Álvarez Website 1 Website 2 E-Mail
Signal Theory and Communication Department, Universidad Rey Juan Carlos; Center for Computational Simulation, Universidad Politécnica de Madrid; Persei Vivarium, Inc. (Madrid, Spain)
Interests: Biomedical Engineering; Signal Processing; Machine Learning; Image Processing; Arrhythmias
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 available this technology 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, but sometimes intimately related, 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, politics 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, specially in healthcare scenarios, where patient data become more than ever the key to improve the patient healthcare.
Healthcare is now an open field to get advantageous use of Deep Learning and Big Data advances, and challenges are open in order to provide with systems that can be accurate enough to be useful to the clinician and the patient in the health itinerary. Not only large amounts of data are available, but also sensitivity and specificity are to be paid special attention, as well as support systems rationally fitting into the health system.
The goal of this Special Issue is to put together relevant contributions, condensed in five key cornerstones of Deep Learning and Big Data applications in healthcare. On the one hand, the applications can include works with medical images (magnetic resonance, radioscopy and tomography, echography, nuclear medicine), contributions to signal processing (cardiac, neural, long-term monitoring, wellness devices), or data from large forms (primary attention, specialized medicine, clinical practice, electronic health recordings, hospital information systems, interoperability).
In addition, companies and organizations are playing a relevant role in this breakpoint, hence their contributions and experience are very welcome, in order to complete the landscape of the progress in the field, as well as open challenges for the research community.
Finally, the feature interpretation remains an open issue in Deep Learning and Big Data state-of-the-art, but it takes special relevance in healthcare applications, in order to gain confidence in their use both by the healthcare staff and by the patients, so contributions including insights into this hot and open topic are welcomed.
Prof. Dr. José Luis Rojo-Álvarez
Manuscript Submission Information
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- Electronic Health Recording