1 October 2016
invites manuscript submissions in the area of Healthcare Big Data.
Healthcare data is rapidly growing with the large volume and multi-dimensional data generation from cyber, physical, and social space. Heterogeneous healthcare data in various forms, such as images, text, video, raw sensor data, etc., are required to be effectively stored, processed, queried, indexed and analyzed. These datasets differ widely in their volume, variety, velocity and value, including patient-oriented data such as electronic medical records (EMR), public-oriented data such as public health data, and knowledge-oriented data such as drug-to-drug, drug-to-disease, disease to disease interaction registries. The healthcare big data brings great challenges while also playing an important role in healthcare transformation. The traditional techniques do not compromise end-users’ Quality of Service (QoS) in terms of data availability, data response delay, etc. It is vital to develop wireless big data communication systems, machine learning techniques and software tools for supporting fast data query, data analytics, and data visualization which can provide high Quality of Experience (QoE) for users.
The progress in this area can be made by applying and extending well-founded formal models and techniques from multiple domains of computer science, such as cloud computing, data mining, machine learning, etc. Unfortunately, current healthcare information systems still lack the successful implementation of big data solutions. This Special Section aims to theme innovative research achievements in the field of big data related techniques and applications for healthcare.
This Special Section in IEEE Access
will bring together academic and industrial researchers to identify and discuss technical challenges and recent results related to healthcare big data. To meet the requirements of healthcare big data, more comprehensive reporting, more efficient monitoring, more effective and professional domain data mining, more advanced medical evaluation, simulation and prediction, new concepts and design approaches are in great need. The big-data revolution is in its early days, and most of the potential for value creation is still unclaimed. But for big-data initiatives to succeed, the healthcare system must undergo some fundamental changes. This Special Section will discuss the analytical capabilities that will be required to capture big data’s full potential, ranging from reporting and monitoring activities that are already occurring to predictive modeling and simulation techniques that have not yet been used at scale and aims to present the latest advances of the fundamental technologies and market trends that will impact the development of healthcare big data.
Topics of interests include, but are not limited to:
- Data Models and Architectures for healthcare
- Healthcare Data Integration and Information Fusion
- Benchmarking of big data infrastructure in healthcare
- Novel algorithms and applications dealing with healthcare data
- Data science and modeling for health analytics
- Advances in storage models for healthcare data variety
- Detection and diagnosis assisted by big data
- Visualization tools and systems for healthcare applications
- QoS optimization techniques for big data healthcare applications
- Healthcare big data applications via 5G tactile internet
- Techniques for preserving security and privacy of healthcare information
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Min Chen, Huazhong University of Science & Technology, China
1. Haiyang Wang, University of Minnesota Duluth, USA
2. Iztok Humar, University of Ljubljana, Slovenia
3. Yin Zhang, Zhongnan University of Economics and Law, China
4. Jiafu Wan, South China University of Technology, China
IEEE Access Editor in Chief:
Michael Pecht, Professor and Director, CALCE, University of Maryland
Contact Associate Editor and submit manuscript to:
For inquiries regarding this Special Section, please contact: Bora M. Onat, Managing Editor, IEEE Access
(Phone: (732) 562-6036, email@example.com