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Algorithms
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Selected papers from: Second International Workshop on Semantic Web Technologies...
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Selected papers from: Second International Workshop on Semantic Web Technologies for Health Data Management

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 9605

Special Issue Editors

Dr. Haridimos Kondylakis

E-Mail Website
Guest Editor
Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Science and Technology Park of Crete, N. Plasthra 100, Vassilika Vouton, GR 700 13 Heraklion, Greece
Interests: big data management; semantic interoperability; data series; information integration; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Kostas Stefanidis

E-Mail Website
Guest Editor
Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Kalevantie 4, 33100 Tampere, Finland
Interests: big data management; personalization; recommender systems; entity resolution; data exploration; data analytics; responsible data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2019, the second International Workshop on Semantic Web Meets Health Data Management (SWH) will be held. The workshop will focus on techniques that enable seamless, effective, and efficient access to the various health datasets and novel methods available for exploiting the existing information. Selected papers from this workshop will be recommended for inclusion in this Special Issue publication; however, new submissions are also welcomed.

Topics of interest include, but are not limited to, the following:

  • Ontologies and data models in the health domain;
  • Semantic integration of heterogeneous health data sources;
  • Wearable and sensor data integration with health data;
  • Web-scale and cloud-based health data management systems;
  • NoSQL and graph databases for health data management;
  • Semantic search and reasoning over health data;
  • Recommendations for health data;
  • Exploratory search of health data via query reformulation, auto-completion, type ahead search, and approximate query-answering;
  • Health data exploration through visualization;
  • Analytics over large-scale health data;
  • Personal health apps;
  • Data quality, profiling, and uncertainty of health data;
  • Data provenance and trust of health data;
  • Data versioning, evolution, change detection, and representation;
  • Generation and aggregation of health semantics;
  • Natural language processing and text mining techniques for health data;
  • Novel techniques for security, privacy, and sharing of health data;
  • Innovative use of semantic technologies for health data;
  • Knowledge graph construction on health data.

Dr. Haridimos Kondylakis
Dr. Kostas Stefanidis
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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.

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Published Papers (2 papers)

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Research

21 pages, 600 KiB  
Article
The Need for Machine-Processable Agreements in Health Data Management
by George Konstantinidis, Adriane Chapman, Mark J. Weal, Ahmed Alzubaidi, Lisa M. Ballard and Anneke M. Lucassen
Algorithms 2020, 13(4), 87; https://doi.org/10.3390/a13040087 - 7 Apr 2020
Cited by 3 | Viewed by 4463
Abstract
Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and [...] Read more.
Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources. Full article
(This article belongs to the Special Issue Selected papers from: Second International Workshop on Semantic Web Technologies for Health Data Management)
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21 pages, 347 KiB  
Article
Multidimensional Group Recommendations in the Health Domain
by Maria Stratigi, Haridimos Kondylakis and Kostas Stefanidis
Algorithms 2020, 13(3), 54; https://doi.org/10.3390/a13030054 - 28 Feb 2020
Cited by 13 | Viewed by 4346
Abstract
Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based [...] Read more.
Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents. Full article
(This article belongs to the Special Issue Selected papers from: Second International Workshop on Semantic Web Technologies for Health Data Management)
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