Special Issue "Health Assessment in the Big Data Era"
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 31 December 2019
Prof. Konstantinos P. Tsagarakis
Business and Environmental Technology Economics Lab, Department of Environmental Engineering, Democritus University of Thrace, Greece
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Interests: technical–economic project evaluation; environmental and energy economics; public health economics; environmental and energy behavior; big data; online behavior; environmental performance of firms; quantitative methods
Dr. František Babič
Centre of Business Information Systems, Department of Cybernetics and Artificial Intelligence, Faculty of electrical ingineering and informatics, Technical University of Košice, Slovakia
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Interests: data analytics; healthcare informatics; knowledge management; clinical decision support systems
Dr. Michal Rosen-Zvi
Healthcare represents an important data source for different purposes, such as supporting diagnostic processes, predicting epidemics, improving quality of life, and avoiding preventable casualties. Traditional Machine Learning or statistical methods for data processing and analysis are no longer sufficient, as they are adapted to new conditions or replaced by novel methods suitable for large volumes of offline data or online continuous data streams. The main objective of this Special Issue is to collect papers with different views and approaches to this domain; methods motivated by the need to improve Healthcare, reduce costs, and achieve more effective diagnostics. In the Big Data Era, the volume of digital information continuously increases, and requires our attention not only from the technological point of view, but from the perspective of trust and ethics as well. The large volumes of data available in this field provide new opportunities to develop various technological solutions, all the while having the patients’ interest as a priority. Automated decision-making in Healthcare must respect existing differences and specific conditions in order to operate properly and correctly. It requires considering a veracity of available data with the strong influence on the reliability of developed methods and tools.
This Special Issue aims at providing selected examples of approaches and case studies where such advanced methods are found beneficial and have a positive impact on patients’ lives. It will be of reference on how Βig Data Analytics can help improve Healthcare, better monitor health and medicine related issues, as well as address the issues of reducing costs and increasing economic benefits.
Prof. Dr. Konstantinos P. Tsagarakis
Dr. František Babič
Dr. Michal Rosen-Zvi
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Data Processing
- Data Analysis
- Data Visualization
- Healthcare IoT
- Smart Networks
- Social Media Data
- Online Behavior
- Clinical Decision Support Systems
- Trust and Ethics
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Early screening for colorectal cancer by big data analysis of trends in complete blood counts
Authors: Gideon Koren MD*, Inbal Goldshtein MSc, Pinchas Akiva PhD, Ran Goshen PhD, Varda Shalev, MD
Abstract: Colorectal cancer must be diagnosed early in order to ensure prompt surgical removal before the disease is spread. Many patients fail to submit a screening stool sample to identify occult blood (FOBT) or to undergo colonoscopy. We describe the evolution of a novel method for the detection of colorectal cancer, by analysis of changes in complete blood counts. In subjects who have not undergone screening with FOBT or colonoscopy, we document the ability to utilize a novel algorithm based on big data analysis, which calculates the risk of colorectal cancer from routine complete blood counts measurements, long before anemia is apparent. The results show values of sensitivity and specificity equivalent to, and even superior to the routine use of FOBT. This has created a unique opportunity to diagnose colorectal cancer cases before symptoms have emerged, when the disease is more likely to be curable.