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Special Issue "Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 11 April 2021.

Special Issue Editors

Prof. Antonella Carbonaro
Website
Guest Editor
Department of Computer Science and Engineering - DISI, Alma Mater Studiorum-Università di Bologna, Bologna, Italy
Interests: customization and content-based information processing for data and knowledge representation; semantic web technologies; personalized environments; heterogeneous data integration from IoT devices
Special Issues and Collections in MDPI journals
Dr. Gianluca Moro
Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
Interests: data mining; text mining; agents & peer-to-peer systems; sensor networks; multi-dimensional indexing

Special Issue Information

Dear Colleagues,

A main transformation that characterizes the era in which we live concerns the high availability of data (especially thanks to the pervasiveness of social media), which is most of the time unstructured, not labeled and expressed in natural language. One of the most investigated areas in this sense is medicine and health, wherein researchers are often called on to put into play cutting-edge analytical techniques, often trying to manage the semantic aspects of the data considered. Cognitive computing systems process enormous amounts of data in order to answer specific queries and make customized intelligent analyses, potentially improving the quality of patient care.

So far, quantitative techniques (such as statistical models, machine learning and deep learning) and qualitative/symbolic techniques (related to the world of the Semantic Web, ontologies and knowledge graphs) have given good results, but the growing complexity of such applications in healthcare has led many experts to assert that the future demands a fusion of these solutions.

This Special Issue, entitled "Role and Challenges of Healthcare Cognitive Computing: From Information Extraction to Analytics", aims to explore the scientific-technological frontiers that characterize the solving of the above-mentioned problems. It seeks original, previously unpublished papers empirically addressing key issues and challenges related to the methods, implementation, results and evaluation of novel approaches based on the use of Cognitive Computing in healthcare.

Prof. Antonella Carbonaro
Dr. Gianluca Moro
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. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). 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

  • Cognitive Computing
  • Natural Language Processing
  • Semantic Web
  • Neural-Symbolic Learning
  • Wearable sensors for medical applications

Published Papers (2 papers)

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Research

Open AccessArticle
A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared
Sensors 2020, 20(24), 7319; https://doi.org/10.3390/s20247319 - 19 Dec 2020
Abstract
On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other [...] Read more.
On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease. Full article
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Open AccessArticle
Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements
Sensors 2020, 20(21), 6293; https://doi.org/10.3390/s20216293 - 05 Nov 2020
Abstract
Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real [...] Read more.
Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. In this work, we concentrated on the basic parameter typically measured by fitness applications and devices—the number of steps taken daily. In particular, the main goal of this study was to compare the accuracy and precision of smartphone applications versus those of wearable devices to give users an idea about what can be expected regarding the relative difference in measurements achieved using different system typologies. In particular, the data obtained showed a difference of approximately 30%, proving that smartphone applications provide inaccurate measurements in long-term analysis, while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous studies reporting data collected with phone-based applications, and besides discussing the current limitations, we support the use of wearable devices for mHealth. Full article
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