Guest Editors
Dr. Farman Ali, Sejong University, Seoul, South Korea
Dr. Shaker EI-Sappagh, Galala University, Egypt
Dr. Muhammad Fazal Ijaz, The University of Melbourne, Australia
Summary
The healthcare industry has been experiencing a rapid increase in the use of IoT-based wearable sensors and social networking. These wearable sensors are used to continuously monitor patients internally and externally to detect chronic diseases like Alzheimer's and heart disease. Social network data is used to identify emotional status and accrued stress, which can affect a patient's health. Although numerous Machine Learning-based healthcare systems have been proposed to monitor chronic patients using these technologies, they are not well-equipped to efficiently consider the characteristics of biomedical data. Biomedical data is unstructured and noisy, which makes it challenging to extract valuable information and accurately analyze it for chronic patient monitoring. Additionally, electronic medical records (EMRs) are also unstructured and constantly increasing in size due to daily medical tests. Therefore, an intelligent system with semantic knowledge is needed to automatically handle the extracted information from biomedical data, analyze it to identify hidden symptoms of chronic disease, and predict the patient's health condition. Furthermore, the healthcare industry requires Deep Learning (DL) models that can process both sensor and textual data (biomedical data) for disease prediction. The aim of this special issue is to address the areas of advanced deep learning modeling and semantic knowledge for intelligent healthcare. These two aspects can help the existing healthcare system to process and analyze unstructured and noisy biomedical data for physicians to diagnose patients. This special issue will explore the new challenges of multitask deep learning models and semantic knowledge in intelligent healthcare. High quality and state-of-the-art research papers on this subject are encouraged to be published in this special issue.
Keywords
The topics of interest for this special issue include, but are not limited to:
• DL model in healthcare recommendation systems.
• AI approaches in healthcare monitoring system.
• DL-based clinical decision support system.
• Multitask DL models for disease prediction.
• DL models for processing the EMR.
• AI-based NLP for biomedical data.
• Ontology-based applications in intelligent healthcare.
• Multitask DL-based social networking data analysis.
• DL for IoT-based healthcare systems.
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