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Data-Enabled Intelligence for Digital Health

Submission Deadline: 31 May 2019

Submission Deadline: 31 May 2019

IEEE Access invites manuscript submissions in the area of Data-Enabled Intelligence for Digital Health.

The worldwide increase in the aging population presents an urgent need for new technologies to improve the quality of life for the elderly. In recent years we have seen rapid development of healthcare technologies along with the widespread use of Internet, mobile technologies, data analytics and artificial intelligence in healthcare. These developments have resulted in highly multi-disciplinary research in digital health and smart health, and have also driven the move towards more personalized care.

Digital health aims to apply data sciences, machine learning, artificial intelligence and the internet of things to tackle the health problems and challenges faced by patients and the care professionals. For example, tracking personalized health indicators regularly such as blood pressure, heart rate and others can help with the management of the health and well-being of patients with heart issues.

New technologies developed in the digital industry, particularly in the emerging interfacing area between big data and artificial intelligence, are changing the way healthcare is delivered and can have an enormous economic impact on healthcare provision. We are experiencing extensive research in health care including the development of new smart sensing, new algorithms, and new systems or devices for personalized healthcare. One of the fundamentals of these developments is to ensure that healthcare data can be accessed and analyzed effectively in order to support accurate decision-making. Most digital health system design has been focused on the functionalities defined by the domain expertise. For these types of systems, user experience and effectiveness of the systems will very much depend on the users’ knowledge of the system. This can be a challenging issue for personalized healthcare, particularly for users with disabilities and in an aging society.

Extensive research is currently taking place worldwide in the related areas, which in return raises new scientific questions as well as practical issues; for example (1) what will the next generation of Artificial Intelligence (AI) provide for us to achieve a better quality of life, particularly for our aging society? (2) How can healthcare systems be data-enabled to exploit a learning capability and fit in personal needs? (3) How can data-enabled technologies support effective human-machine cooperation and adapt to each other, and ultimately support humans and machines to work together? and (4) How can human-machine cooperation drive new intelligence to improve the quality of life for people in the healthcare systems?

This Special Section in IEEE Access aims to attract original research articles that advance the state of the art in digital health as well as data science and artificial intelligence. The goal is that it provides an opportunity for us to gain a significantly better understanding of the current developments and the future direction of artificial intelligence and data science in relation to healthcare.

The topics of interests include, but are not limited to:

  • New technologies and frameworks that support human-machine interaction and human-machine collaborative intelligence
  • Brain-Computer modeling for human-machine cooperation
  • Cognitive computing for healthcare and data intelligence
  • Brain-Computer modeling for cognitive intelligence
  • The design and implementation of personalized healthcare systems
  • The value and challenges of human-machine collaboration in healthcare
  • Data Science and artificial intelligence in digital health, and health management
  • Data science and artificial intelligence in public health
  • Machine learning to understand human behavior and well-being
  • New algorithms for medical and healthcare data analytics
  • Predictive analysis in personalized healthcare
  • Intelligent and predictive analytics for early warning, feedback and in-time intervention for personalized healthcare
  • The cutting edge development of digital health
  • New digital technologies to assist mental healthcare
  • New technology to enable personal data security and effective use in healthcare

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Yonghong Peng, University of Sunderland, UK

Guest Editors:

  1. Wenbing Zhao, Cleveland State University, United States
  2. Yongtao Hao, Tongji University, China
  3. Yongqiang Cheng, University of Hull, United Kingdom
  4. Linbo Qing, Sichuan University, China
  5. Weihong Huang, Xiangya Hospital, China
  6. Ying Song, West China Hospital, China

 

Relevant IEEE Access Special Sections:

  1. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  2. Mobile Multimedia for Healthcare
  3. Healthcare Big Data


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

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

For inquiries regarding this Special Section, please contact: Yonghong.Peng@Sunderland.ac.uk