Healthcare represents an important data source for different purposes, such as supporting diagnostic processes, predicting epidemics, improving quality of life, and avoiding preventable casualties. Traditional Machine Learning or statistical methods for data processing and analysis are no longer sufficient, as they are adapted to new conditions or replaced by novel methods suitable for large volumes of offline data or online continuous data streams. The main objective of this Special Issue is to collect papers with different views and approaches to this domain; methods motivated by the need to improve Healthcare, reduce costs, and achieve more effective diagnostics. In the Big Data Era, the volume of digital information continuously increases, and requires our attention not only from the technological point of view, but from the perspective of trust and ethics as well. The large volumes of data available in this field provide new opportunities to develop various technological solutions, all the while having the patients’ interest as a priority. Automated decision-making in Healthcare must respect existing differences and specific conditions in order to operate properly and correctly. It requires considering a veracity of available data with the strong influence on the reliability of developed methods and tools.
This Special Issue aims at providing selected examples of approaches and case studies where such advanced methods are found beneficial and have a positive impact on patients’ lives. It will be of reference on how Βig Data Analytics can help improve Healthcare, better monitor health and medicine related issues, as well as address the issues of reducing costs and increasing economic benefits.
More information are available here .
Prof. Dr. Konstantinos P. Tsagarakis
Dr. František Babič
Dr. Michal Rosen-Zvi