Submission Deadline: 30 April 2022 (closed)
The emerging fields in which most clinical procedures and disorders have been addressed with the newest Artificial Intelligence and advanced Statistical Learning are Medical Applications. Big Data poses major deep learning challenges, including, among many others, large size, heterogeneity, noisy labels, and non-stationary distribution. We need to overcome these technological problems with new ways of thinking and transformative strategies in order to achieve Big Data's full potential. It has provided instruments for the collection, management, analysis and integration of large amounts of diverse, structured and unstructured data produced by existing healthcare systems.
The process of clinical data and diagnostic procedures for different medical conditions can be automated, which can help to enhance the diagnosis of medical care. Medical data addresses many issues, including the lack of accessibility of sophisticated large-scale databases, high-dimensional samples and class imbalances.
This special issue focuses high-quality papers from academics and medicine- or industry-related researchers of deep learning studies on current healthcare systems with analytical patterns of diseases using novel AI and applied mathematical algorithms.
Topics (but not limited to):
• AI based applications in Healthcare, biomedical and bioscience
• Algorithms for Natural language processing and clinical pattern recognitions
• Big data based Medical diagnosis using sensor analytical patterns
• Biomedical Imaging and Data Visualization
• Behavioural, Environmental, and Public Health Informatics
• Clinical and Health Decision Support Systems
• Cross-Media Methods for Big Data Representation in medical applications
• Deep learning and its applications in medicine
• Data breach prevention and security
• Data inference, mining, and trend analysis
• Health information systems and convergence of health
• Healthcare modeling and simulation
• Intelligent systems for medical applications
• Internet of Medical Things (IoMedT)
• Optimization of AI architectures and designing new loss functions
• Predictive Modeling and Analytics in Healthcare
• Sensor-based mHealth apps
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