全文截稿: 2021-11-30
影响因子: 4.873
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
• 小类 : 计算机：跨学科应用 - 2区
网址：
http://www.journals.elsevier.com/applied-soft-computing/
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.
Topics
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:
Fuzzy systems for predicting and monitoring the spread of epidemic diseases
Fuzzy systems for measuring the damage of the epidemic disease
IT2 fuzzy sets for uncertain healthcare datasets
Fuzzy approaches for neuroimaging and functional brain imaging processing of COVID-19
Fuzzy learning models for feature extraction of COVID-19
Fuzzy medicine and healthcare data mining based on the Hadoop or Spark platforms
Fuzzy system for patient planning and health services
Multi-objective evolutionary and adaptive fuzzy systems for handling epidemic disease
Fuzzy models for medical image classification/ diagnosis /recognition
Fuzzy data mining for brain-machine interfaces and medical signal analysis
Fuzzy classification for multi-modality image fusion for analysis, diagnosis, and intervention
Real-world applications of fuzzy system for future challenges of COVID-19
