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IssuesSpecial Issues
Human-centric Computing and Information Sciences (HCIS) Korea Information Processing Society-Computer Software Research Group (KIPS-CSWG)(ISSN: 2192-1962)

(IF 5.900) Special issue on

Machine Learning and Blockchain Techniques Driven Medical Analysis

With billions of mobile devices in use worldwide, the cost of medical device connectors and sensors has fallen dramatically, and recording and transmitting medical data has never been easier. However, the transformation of physiological data into clinical information of real value requires artificial intelligence algorithms to make it happen. Processing the big data implicit in biomedical time series and images, accounting for individual differences, identifying and extracting characteristic patterns of health function and translating these patterns into clinical information with guidance needs to be supported by an adequate knowledge base of physiology, advanced digital signal processing capabilities and machine learning (e.g. deep learning) skills.

The creation of intelligent algorithms combined with novel wearable portable biosensors offers unprecedented possibilities and opportunities for remote patient monitoring (i.e. non-traditional clinical settings) and condition management. Although traditional sharing schemes such as databases and cloud storage can provide sufficient capability to share sample data in a timely manner, they cannot guarantee the immutability of data and the intellectual property rights of research results. Fortunately, the emerging blockchain techniques can help users manage data easier and more secure.

This special issue will focus on various aspects of information processing and blockchain techniques, including data pre-processing, visualisation, regression, dimensionality reduction, function selection, classification (LR, SVM, NN) and its role in healthcare decision support.

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

Predicting individual patient outcomes

Mining, processing and making sense of clinical notes

Patient risk stratification

Parsing biomedical literature

Bio-marker discovery

Brain imaging technologies and related models

Feature selection/dimensionality reduction

Text classification and mining for biomedical literature

Exploiting and generating ontologies

ML systems that assist with evidence-based medicine

Management and acknowledgement of medical devices

Efficient transfer of medical data using blockchain

Robust, decentralised medical treatment enhancements

Privacy-preserving analysis of blockchain

Decentralised monitoring of IoT health care devices

Medical blockchain anonymity

Innovative technologies for flexible operations of heath care sectors

Scalability, security and robust blockchain

Big data and blockchain management

Advancement and fraud identification in medical claims

Biomedical advances and related privacy-preserving analysis

Important Dates

Submissions open : 20 December 2021

Submission deadline : 30 April 2022

Author notification : within 4 weeks after submission

Revised manuscript due : within 2 weeks after notification

Notification of acceptance : within 2 weeks after revision submission

Tentative accepted paper publication date : within 2 months after final version

Tentative SI paper collection and its web open : 3rd / 4th Quarter, 2022 (TBA)

Submission Guideline

All submitted papers must be clearly written in excellent English and contain only original work. All papers must be submitted in an electronic format, e.g., PDF format (preferred) or MS Word. Manuscripts should follow the formatting of the sample manuscript and references. You can refer to the details in the submission menu http://hcisj.com/submission/preparing_manuscript.php All papers and some supplementary materials should be submitted through ScholarOne Manuscripts. The authors must select “SI2021-09ML Blockchain-Medical Analysis'', when they reach the “Article Type” step in the submission process https://mc04.manuscriptcentral.com/hcis

Guest Editors

Dr. Thippa Reddy Gadekallu (Managing Guest Editor), School of Information Technology and Engineering, Vellore Institute of Technology, India. thippareddy.g@vit.ac.in (Lead GE)

Dr. Mamoun Alazab, Information Technology and Environment, Charles Darwin University, Australia. alazab.m@ieee.org

Dr. Mounir Ghogho (IEEE Fellow), College of Engineering and Architecture, International University of Rabat, Sale, Morocco. mounir.ghogho@uir.ac.ma

Dr. Chunhua Su, Division of Computer Science, University of Aizu, Fukushima, Japan. chsu@u-aizu.ac.jp

Dr. Saqib Hakak, Assistant Professor, Computer Science, University of New Brunswick, Canada. saqib.hakak@unb.ca

Sivarama Krishnan Rajaraman, Research Scientist, Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Maryland, USA. sivaramakrishnan.rajaraman@nih.gov