Special Issue: Recent Advances of Knowledge Discovery in Clinical Emergency Medicine
Guest Editors
Dr. Man-Fai Leung
Anglia Ruskin University, Cambridge, UK
Email: man-fai.leung@aru.ac.uk
Dr. Hangjun Che
College of Electronic and Information Engineering, Southwest University, Chongqing, China
Email: hjche123@swu.edu.cn
Dr. Keping Yu
Graduate School of Science and Engineering, Hosei University, Tokyo, Japan
Email: keping.yu.47@hosei.ac.jp
Dr. Shimin Wang
Department of Chemical Engineering, Queen's University
Email: shimin.wang@queensu.ca
Manuscript Topics
Due to the astronomical increase in computer processing power, the rapid development of artificial intelligence (AI) has attracted considerable attention. The impressive performance of AI in dealing with many different daily life applications has shown great potential to significantly impact the field of emergency medicine. For example, the application of AI techniques (machine learning, deep learning, etc.) provides efficient tools to assisted systems such as medical diagnostics and patient monitoring. The techniques can be used as a screening tool or as an aid to diagnosis for fast and informed decisions in order to improve the operational efficiencies and quality of healthcare.
Emergency medicine (EM) is a medical discipline that focuses on the theory and practice of life-threatening diseases or the care for human beings with urgent healthcare needs. The application of AI techniques in EM (such as prioritization and disposition, emergency department operations, and patient monitoring) can greatly improve the medical treatment efficacy, effectiveness and efficiency. Although both AI and EM have sophisticated development, the application of AI in EM is still under-developed.
The aim of this Special Issue is to bring together academic and industrial practitioners to exchange and discuss the latest AI techniques and applications in emergency medicine. Original research and review articles are welcome.
Potential topics include but are not limited to the following:
• AI techniques for healthcare analytics in emergency medicine
• AI techniques for prioritization and disposition, emergency department operations, and patient monitoring
• AI-based systems for emergency department
• Optimization Algorithms for emergency medicine
• Trustworthy AI in healthcare
• Robustness for complex medical decision making
• AI techniques for pattern recognition in emergency medicine
• Data mining and knowledge discovery techniques in healthcare
• AI techniques for medical image registration
• Internet of Things in Emergency Medical Care and Services
• Machine learning and deep learning in emergency medicine
Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/
Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication. The deadline for manuscript submission is 30 June 2023
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