Guest Editors
Prof. Farman Ali, Sungkyunkwan University, South Korea
Prof. Shaker EI-Sappagh, Galala University, Egypt
Prof. Daehan Kwak, Kean University, USA
Summary
The healthcare industry has been experiencing a rapid increase in the use of digital devices and social networking. These digital devices 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 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 models with IoT technology 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 IoT-based devices 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 deep learning models and IoT-based sensors 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
• IoT-based Chronic Disease Monitoring.
• DL model in healthcare recommendation systems.
• IoT-based healthcare monitoring system.
• DL-based clinical decision support system.
• IoT-based cloud system for disease prediction.
• IoT-based wireless sensors networks and its application for smart healthcare.
• AI-based NLP in IoT-based healthcare.
• Ontology-based applications in intelligent healthcare.
• The role of social networking data in smart healthcare.
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