Impact Score 9.24
Managing Guest Editor
Weiping Ding, Nantong University, China, Email: [email protected]
Javier Andreu Perez, University of Essex, United Kingdom, Email: [email protected]
Yiu-ming Cheung, Hong Kong Baptist University, Hong Kong, China, Email: [email protected]
Swagatam Das, Indian Statistical Institute, India, Email: [email protected]
Xiaodong Yue, Shanghai University, China, Email: [email protected]
Dariusz Mrozek, Silesian University of Technology, Poland, Email: [email protected]
Scope of the issue
The world issues to deal with the pandemic caused by the pathogen SARS-CoV-2 has urgently posed the need of rethinking the available resources to combat a health crisis of this dimensions. Innovation in healthcare needs to be accelerated to address the health problems of our time and the future. Biomedical and healthcare data are available in different formats, including numeric, textual reports, images, and the data may come from different sources. A major challenge in biomedical science and healthcare involves coping with the uncertainty, imprecision and incompleteness. Such uncertainties make it difficult to develop useful models, algorithms, systems, and realizing their successful applications. Although the current research in this field has shown promising results, there is an urgent need to explore novel data-driven knowledge discovery and analytics methods in clinical research to improve epidemic monitoring and healthcare delivery as a whole. Intelligent medicine and healthcare decision support systems have become an emerging research topic since they can be applied for disease diagnostics and/or prevention, follow-up monitoring, defining treatment pathways, clinical decision support etc.
Despite the significant recent advances in medicine and healthcare data analysis, there are substantial research challenges and open questions to be explored. These demand further and deeper investigations to develop more useful decision-making systems that are capable of dealing with randomness, imprecision, volume, vagueness, incompleteness, and missing values along with efficient handling of variety, velocity and (abundant or lacking) volume of biomedical data. Compared to the traditional decision support techniques, the representation of fuzzy linguistic terms based on soft computing provides a straightforward framework for building more understandable, imprecision-aware clinical systems. As opposed to systems powered by statistical reasoning only, fuzzy biomedical systems cater a way of building models that encode the imprecise conceptual semantics of a health problem, not just for doing analytics, but also to embrace its interpretability. Thus, designing an efficient and effective fuzzy system to deal with uncertainty is an emerging and promising topic to improve reasoning and intelligent monitoring, control, diagnostic and treatment in biomedical science in healthcare.
In this special issue we will consider submissions in that deal with innovative research works that face contemporary research issues of biomedical engineering in healthcare. Articles that are not innovative enough in their clinical research questions and applications will not be considered for publication in this special issue.
The topics include but are not limited to:
Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at www.editorialmanager.com/asoc/
All the papers will be peer reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Each submission must contribute to fuzzy theory related methodology. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will be desk-rejected, and will not be sent for review.
Authors should select "VSI: FSBSH" when they reach the "Article Type" step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues.