Special Issue "Health Assessment in the Big Data Era"

Special Issue Editors

Guest Editor
Prof. Dr. Konstantinos P. Tsagarakis

Business and Environmental Technology Economics Lab, Department of Environmental Engineering, Democritus University of Thrace, Greece
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Phone: +30 2541079397
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
Guest Editor
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
Guest Editor
Dr. Michal Rosen-Zvi

Healthcare informatics, IBM Research, Haifa Research Labs, Israel
Website | E-Mail
Interests: healthcare informatics; machine learning for healthcare; clinical genomic analysis; clinical decision support systems; deep learning for patients; health behavior; causal inference of healthcare data

Special Issue Information

Dear Colleagues,

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
Guest Editors

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.

Keywords

  • Data Processing
  • Data Analysis
  • Data Visualization
  • Healthcare IoT
  • Smart Networks
  • Social Media Data
  • Online Behavior
  • Clinical Decision Support Systems
  • Trust and Ethics

Published Papers (2 papers)

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Research

Open AccessArticle Constrained Optimization-Based Extreme Learning Machines with Bagging for Freezing of Gait Detection
Big Data Cogn. Comput. 2018, 2(4), 31; https://doi.org/10.3390/bdcc2040031
Received: 4 September 2018 / Revised: 9 October 2018 / Accepted: 12 October 2018 / Published: 15 October 2018
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Abstract
The Internet-of-Things (IoT) is a paradigm shift from slow and manual approaches to fast and automated systems. It has been deployed for various use-cases and applications in recent times. There are many aspects of IoT that can be used for the assistance of
[...] Read more.
The Internet-of-Things (IoT) is a paradigm shift from slow and manual approaches to fast and automated systems. It has been deployed for various use-cases and applications in recent times. There are many aspects of IoT that can be used for the assistance of elderly individuals. In this paper, we detect the presence or absence of freezing of gait in patients suffering from Parkinson’s disease (PD) by using the data from body-mounted acceleration sensors placed on the legs and hips of the patients. For accurate detection and estimation, constrained optimization-based extreme learning machines (C-ELM) have been utilized. Moreover, in order to enhance the accuracy even further, C-ELM with bagging (C-ELMBG) has been proposed, which uses the characteristics of least squares support vector machines. The experiments have been carried out on the publicly available Daphnet freezing of gait dataset to verify the feasibility of C-ELM and C-ELMBG. The simulation results show an accuracy above 90% for both methods. A detailed comparison with other state-of-the-art statistical learning algorithms such as linear discriminate analysis, classification and regression trees, random forest and state vector machines is also presented where C-ELM and C-ELMBG show better performance in all aspects, including accuracy, sensitivity, and specificity. Full article
(This article belongs to the Special Issue Health Assessment in the Big Data Era)
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Open AccessArticle The Internet and the Anti-Vaccine Movement: Tracking the 2017 EU Measles Outbreak
Big Data Cogn. Comput. 2018, 2(1), 2; https://doi.org/10.3390/bdcc2010002
Received: 26 November 2017 / Revised: 11 January 2018 / Accepted: 13 January 2018 / Published: 16 January 2018
Cited by 4 | PDF Full-text (9322 KB) | HTML Full-text | XML Full-text
Abstract
In the Internet Era of information overload, how does the individual filter and process available knowledge? In addressing this question, this paper examines the behavioral changes in the online interest in terms related to Measles and the Anti-Vaccine Movement from 2004 to 2017,
[...] Read more.
In the Internet Era of information overload, how does the individual filter and process available knowledge? In addressing this question, this paper examines the behavioral changes in the online interest in terms related to Measles and the Anti-Vaccine Movement from 2004 to 2017, in order to identify any relationships between the decrease in immunization percentages, the Anti-Vaccine Movement, and the increased reported Measles cases. The results show that statistically significant positive correlations exist between monthly Measles cases and Google queries in the respective translated terms in most EU28 countries from January 2011 to August 2017. Furthermore, a strong negative correlation (p < 0.01) exists between the online interest in the term ‘Anti Vaccine’ and the Worldwide immunization percentages from 2004 to 2016. The latter could be supportive of previous work suggesting that conspiracist ideation is related to the rejection of scientific propositions. As Measles require the highest immunization percentage out of the vaccine preventable diseases, the 2017 EU outbreak could be the first of several other diseases’ outbreaks or epidemics in the near future should the immunization percentages continue to decrease. Big Data Analytics in general and the analysis of Google queries in specific have been shown to be valuable in addressing health related topics up to this point. Therefore, analyzing the variations and patterns of available online information could assist health officials with the assessment of reported cases, as well as taking the required preventive actions. Full article
(This article belongs to the Special Issue Health Assessment in the Big Data Era)
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Planned Papers

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. 

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