Special Issue "Data Science for Healthcare Intelligence"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 3323

Special Issue Editors

Prof. Dr. Miltiadis D. Lytras
E-Mail Website
Guest Editor
School of Business, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi Athens, Greece
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anna Visvizi
E-Mail Website
Guest Editor
1. School of Business & Economics, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi, Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Interests: smart cities; migration; innovation networks; international business; political economy; economic integration; politics; EU; Central Europe; China
Special Issues, Collections and Topics in MDPI journals
Dr. Ryan Wen Liu
E-Mail Website
Guest Editor
Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: big data; computational transportation science; computer vision; data science; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health is our lifelong goal. The world has experienced severe medical issues, such as an aging population, medical staff shortage and poor quality of life. Thanks to those who have devoted efforts to contributing to the healthcare industries, the advancement of big data, Internet of Things (IoT) and artificial intelligence (AI) have brought a glimmer of success in data science for healthcare applications.                           

In today’s era of data explosion, there are growing and unlimited health data that need to be collected, stored, transferred, investigated, analyzed and utilized. Data science plays a lead role in healthcare intelligence applications. It aims at enabling researchers and enterprises to formulate processing and analysis methods to extract latent information from multiple data resources and to leverage a broad range of data handling and computational platforms.

Medical workers may argue that there may be a conflict of interest between data science and their role, but this is not true. On the contrary, the two are not only able to co-exist but even complement each other. First, the current workload of medical workers (ratio of workers to patients) is heavy but can be reduced through the use of data science. Second, automatic systems focus on routine works so that medical workers can devote more time to professional consultation and surgery activities. Third, the increase in quality of medical services will lead to higher acceptance and satisfaction by the public. Thus, medical workers will achieve a higher social status and better job satisfaction. It is thus evident that the use of data science in the medical field is a win-win situation.

This Special Issue is intended to report high-quality research on recent advances towards data science for healthcare intelligence, more specifically state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of those convergence technologies for providing insights into healthcare intelligence. Topics include but are not limited to:

  • Data warehouse and data mining for healthcare intelligence;
  • Emerging data science techniques for healthcare intelligence;
  • Descriptive, diagnostic, predictive and prescriptive analytics for healthcare applications;
  • Big data, cloud and the IoT platform and architecture;
  • Data quality, privacy, policy and security;
  • Shallow learning and deep learning for healthcare intelligence;
  • Political economy in healthcare;
  • Machine learning algorithms for disease diagnosis;
  • Semantic web technologies in healthcare;
  • Natural language processing in healthcare;
  • Virtual reality and augmented reality in healthcare;
  • Clinical informatics, bioinformatics, imagining informatics, consumer health informatics, research informatics and public health informatics.
Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Prof. Anna Visvizi
Dr. Ryan Wen Liu
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 submissions that pass pre-check are 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. Applied Sciences 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 2300 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.

Published Papers (3 papers)

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Research

Article
A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing
Appl. Sci. 2020, 10(20), 7092; https://doi.org/10.3390/app10207092 - 12 Oct 2020
Viewed by 804
Abstract
Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to [...] Read more.
Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to complete the analysis and achieve goals. Unfortunately, there is no general or standardized integration process; the nature of an integration process depends on the data type, domain, and integration purpose. Based on these parameters, we proposed, implemented, and tested a big data integration framework that integrates big data in the biology domain, based on the domain ontology and using distributed processing. The integration resulted in the same result as that obtained from the local integration. The results are equivalent in terms of the ontology size before the integration; in the number of added items, skipped items, and overlapped items; in the ontology size after the integration; and in the number of edges, vertices, and roots. The results also do not violate any logical consistency rules, passing all the logical consistency tests, such as Jena Ontology API, HermiT, and Pellet reasoners. The integration result is a new big data source that combines big data from several critical sources in the biology domain and transforms it into one unified format to help researchers and specialists use it for further research and analysis. Full article
(This article belongs to the Special Issue Data Science for Healthcare Intelligence)
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Article
Deep-Learning-Based Models for Pain Recognition: A Systematic Review
Appl. Sci. 2020, 10(17), 5984; https://doi.org/10.3390/app10175984 - 29 Aug 2020
Cited by 6 | Viewed by 1194
Abstract
Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges such as feature [...] Read more.
Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years. Furthermore, it presents the major deep-learning methods used in the review papers. Finally, it provides a discussion of the challenges and open issues. Full article
(This article belongs to the Special Issue Data Science for Healthcare Intelligence)
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Article
Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test
Appl. Sci. 2020, 10(14), 4972; https://doi.org/10.3390/app10144972 - 19 Jul 2020
Cited by 1 | Viewed by 868
Abstract
This paper proposes a data-driven knowledge-based system with which aged people can measure the degree of activities of daily living (ADL) by themselves. The proposed system, called E-coach for ADL Test (EAT), provides participants with self-measurement procedures, using e-coaching, which is a guidance [...] Read more.
This paper proposes a data-driven knowledge-based system with which aged people can measure the degree of activities of daily living (ADL) by themselves. The proposed system, called E-coach for ADL Test (EAT), provides participants with self-measurement procedures, using e-coaching, which is a guidance mechanism to lead the participants from an initial stage to a target goal. The EAT traces the behavior of the participants to gather ADL data that tell how well they perform the given e-coaching. Driven by the Internet of Things data, the knowledge-based inference of the EAT carries out the e-coaching mechanism that figures out what state the self-measurement procedures stay on and what guidance is necessary for the next state. The EAT ensures that all the procedures for ADL measurement are executed automatically without any help from medical professionals. The experiment described in this paper demonstrates that the EAT distinguishes between dementia patients and normal people. The measurement report assists medical doctors in the diagnosis of certain medical conditions that these people may have. Full article
(This article belongs to the Special Issue Data Science for Healthcare Intelligence)
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