Big Data is on everyone’s lips, even in healthcare. However, it is not always clear how the scientific data collected can be used in the medical and health sector. The first step is to understand what information can be obtained, thanks to Data Science methodologies, about citizens’ preferences and concerns, and then to be able to use it to offer an even more effective health experience and promote research. This is the challenge of using Data Science in Healthcare, even closer to a promise than to reality.
Once Healthcare Data is made available to the companies, organisations, Data Scientists, the problem arises of how to use it.
The potential of data in healthcare is enormous and those who have been studying the sector for years are convinced that it can actually innovate and improve healthcare profoundly. The positive fallout will, of course, be on both the patient and healthcare facilities. Knowing more information on his state of health would allow everyone to be followed in an increasingly personal and “tailor-made” way.
However, the limits, mostly related to data acquisition, should not be underestimated. The still limited use of electronic medical records (HER), often due to the difficulty of use, limits the collection and sharing of digital e-health data. Another problem is that the data on which the algorithm learning is based derives from common medical practice, which however does not always correspond to the ideal solution for the patient. Besides, the data are not collected uniformly for all sections of the population, just think of the differences in treatment that American patients have based on the possibility or not of having health insurance.
The objective of this special issue is to attract high-quality research and survey articles that promote research and reflect the most recent advances in addressing Data Science methodologies and applications for Healthcare.
We welcome researchers from both academia and industry to provide their state-of-the-art technologies and ideas covering all aspects of Data Science methodologies and applications for Healthcare.
Potential topics include but are not limited to following:
Artificial Intelligence models for Healthcare.
Machine Learning models for Healthcare.
Clustering and classification algorithms for Healthcare.
Deep and reinforcement learning for Healthcare.
Big Data analytics for data processing from Healthcare.
Fuzzy Systems proposals for Healthcare.
Expert/hybrid Systems for Healthcare
AI/ML for IoT, Industry 4.0 for Healthcare
Intelligent security proposals for Healthcare.
Control systems developments for Healthcare
Organization Based Multiagent Systems for Healthcare
Submission Deadline: 30 Jan 2020
Acceptance Notification: 30 Apr 2020
Dr. Francesco Piccialli, Ph.D. (LEADING GE)
University of Naples FEDERICO II, Italy
Prof. Nik Bessis
Edge Hill University, UK
Prof. Gwanggil Jeon
Incheon University, Seoul, South Korea