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NEWS ARTICLE | 25 March 2021

Call for papers: Big Data eHealth applications

The upcoming Special Issue of the Applied Sciences journal issued a call for papers that will be focused on technologies and methodologies in the field of Big Data Analytics for eHealth, also considering their combined use with Internet of Thing (IoT) devices, Artificial Intelligence (AI) methods, and Cyber Security (CS) techniques for the definition of complex systems and architectures for the eHealth domain. A special regard will be paid to papers devoted to the analysis of COVID-19-related Big Data.

In the last few years, the rapid growth of the available digitised medical data has opened new challenges for the scientific research community in the healthcare informatic field. In this scenario, the constantly increasing volume of medical data, as well as the complexity and heterogeneity of this kind of data, requires innovative Big Data Analytics methods for extracting valuable insights from them and, at the same time, these new approaches must also guarantee the required levels of privacy and security. These solutions must provide effective and efficient tools to support the daily routine of physicians, medical professionals, and policy makers, improving the quality of the healthcare systems. The recent pandemic emergency has made the need for new Big Data approaches for the processing of such data more urgent.

Contributions can focus on architectures, algorithms, methods; survey papers and reviews are also welcomed.

The main topics include, but are not limited to:
• eHealth
• Medical Informatics
• Big data Analytics for eHealth
• Biomedical Big Data Analytics
• COVID-19 Big Data Analysis
• Artificial Intelligence in Medicine
• Biomedical Big Data Mining
• Health Information Systems
• Complex Big Data Architectures
• Data-driven Methods
• Security and Privacy of Medical Data
• Machine and Deep Learning approaches for Health Data
• Diagnoses and Therapy Support Systems

The official submission deadline is 15 September 2021.

Manuscript Submission Information

Metadata

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