Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things

Submission Deadline: 31 October 2018

IEEE Access invites manuscript submissions in the area of Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things.

The recent advancement in Internet of Medical Things (IoMT) paradigm aims to enrich our perception of healthcare reality, and incorporating new technologies for such applications. In the context of the IoMT, several medical devices connected to healthcare IT infrastructure can offer superior and more personalized health services. The combination of IoMT data, machine learning, streaming analytics distributed computing, and biomedical systems has become more powerful by enabling the storage and analysis of more data and many different types of data much faster. Machine learning plays a crucial role in the medical imaging field, comprising computer-aided diagnosis, registration and fusion, image segmentation, image-guided therapy, and image database retrieval for providing a better understanding of medical data applied to biomedical systems in IoMT. Moreover, the potential of big data in IoMT is a critical concern to constructing and running the kinds of big data analytics applications are obligatory for IoMT data. Thus it necessitates key focus from academia and industries.

Medical data is central to the IoMT paradigm: from acquiring critical medical sensor data or imaging data to analyzing, processing, and storing of health information, which adds new insights to our view of the world. Machine learning is essential to challenges related to the data source applied to biomedical devices using IoMT. Machine learning and data-driven methods represent a paradigm shift, and they are bound to have a transformative impact in the area of medical data and imaging processing. Many challenges arise as the IoMT permeates our world, especially for low-power resource-constrained devices for accumulating patient’s data, medical data integrity, privacy and security, and network lifetime and quality of service among others. The primary goal of this Special Section in IEEE Access is to provide an overview of the current state-of-the-art advances in machine learning of data source for understanding IoMT.

Topics of interest include, but are not limited to:

• Computer-aided detection or diagnosis applied to biomedical systems in IoMT
• New imaging modalities or methodologies for IoMT
• Innovative machine-learning algorithms or applications in IoMT
• Medical data security and privacy techniques for healthcare
• Energy harvesting and big data analytics strategies in IoMT
• Deep learning for optimizing medical big data in IoMT
• Low-power resource-constrained medical devices for IoMT
• Associative rule learning and reinforcement learning in IoMT
• Smart medical systems based on cloud-assisted body area networks
• Flexible and wearable sensors for prognosis and follow-up based on IoMT Paradigm
• Healthcare Informatics to analyze patient health records, for enabling better clinical decision making and improved healthcare outcomes

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Associate Editor: Kelvin KL Wong, Western Sydney University, Australia

Guest Editors:

1. Dhanjoo N Ghista, University 2020 Foundation, USA
2. Giancarlo Fortino, University of Calabria (Unical), Italy
3. Wanqing Wu, Chinese Academy of Sciences, China

Relevant IEEE Access Special Sections:

1. Mobile Multimedia for Healthcare
2. Health Informatics for the Developing World
3. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: Kelvin.Wong@westernsydney.edu.au