Computational Intelligence in Healthcare

Dear Colleagues,

The amount of patient health data has been estimated to reach 2314 Exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such vast amounts of data; thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast amounts of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data and processes. The use of CI in healthcare can improve the management of clinical disease by introducing intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well for analysis of administrative processes.

This Special Issue will publish original research, overviews, and applications of CI methods for Healthcare. Areas of interest include, but are not limited to the following:

• Fuzzy logic and fuzzy models for healthcare
• Evolutionary computing for healthcare
• Artificial neural networks for healthcare
• Probabilistic models for healthcare
• CI and Big data in healthcare
• Data mining in healthcare
• CI applications in healthcare

Prof. Giovanna Castellano
Dr. Gabriella Casalino
Guest Editors

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