Special Issue on Big Data for eHealth Applications

In the last few years, the rapid growth in available digitised medical data has opened new challenges for the scientific research community in the healthcare informatics field. In this scenario, the constantly increasing volume of medical data, as well as the complexity and heterogeneity of this kind of data require innovative approaches based on Big Data Analytics (BDA) and Artificial Intelligence (AI) methods for extracting valuable insights [1,2,3,4,5], and at the same time, these new approaches must also guarantee the required levels of privacy and security [6]. These solutions must also provide effective and efficient tools for supporting the daily routine of physicians, medical professionals, and policy makers, improving the quality of healthcare systems. Finally, they should leverage the huge amount of information buried under these Big Data [7], exploiting, in this way, their full potential. Furthermore, new heterogeneous and extensive COVID-related datasets have been collected during the recent pandemic and have often been made available to the scientific community. In this case, the need for new and specific Big Data approaches for processing such data makes exploiting these data and providing new and innovative approaches for facing the COVID-19 pandemic more urgent [8,9,10]. In this Special Issue, some innovative applications, tools, and techniques specifically tailored to address issues related to the eHealth domain by leveraging BDA methodologies are presented. Moreover, these techniques are also presented in this Special Issue, given the definition of complex systems and architectures for the eHealth domain fundamentally based on the combination of Internet of Things (IoT) devices and Artificial Intelligence (AI) methods. Finally, the Cyber Security (CS) for eHealth topic is also addressed given the significant increase in cyber threats in the healthcare sector during the last few years.