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Evaluation of Linked, Open Data Sources for Mining Adverse Drug Reaction Signals

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Book cover Internet Science (INSCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10673))

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Abstract

Linked Data is an emerging paradigm of publishing data in the Internet, accompanied with semantic annotations in a machine understandable fashion. The Internet provides vast data, useful in identifying Public Health trends, e.g. concerning the use of drugs, or the spread of diseases. Current practice of exploiting such data includes their combination from different sources, in order to reinforce their exploitation potential, based on unstructured data management practices and the Linked Data paradigm. In this paper, we present the design, the challenges and an evaluation of a Linked Data model to be used in the context of a platform exploiting social media and bibliographic data sources (namely, Twitter and PubMed), focusing on the application of Adverse Drug Reaction (ADR) signal identification. More specifically, we present the challenges of exploiting Bio2RDF as a Linked Open Data source in this respect, focusing on collecting, updating and normalizing data with the ultimate goal of identifying ADR signals, and evaluate the presented model against three reference evaluation datasets.

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Notes

  1. 1.

    https://www.w3.org/RDF/.

  2. 2.

    https://www.w3.org/OWL/.

  3. 3.

    http://www.hermit-reasoner.com/.

  4. 4.

    https://www.w3.org/wiki/HCLSIG/LODD/Interlinking.

  5. 5.

    https://github.com/bio2rdf/bio2rdf-scripts/wiki/RDFization-Guide.

  6. 6.

    https://www.openphacts.org/.

  7. 7.

    https://www.w3.org/Submission/SWRL/.

  8. 8.

    http://data.bioontology.org.

  9. 9.

    https://virtuoso.openlinksw.com/rdf/.

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Author information

Authors and Affiliations

  1. Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Pantelis Natsiavas & Nicos Maglaveras

  2. Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece

    Pantelis Natsiavas, Nicos Maglaveras & Vassilis Koutkias

Authors
  1. Pantelis Natsiavas
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  2. Nicos Maglaveras
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  3. Vassilis Koutkias
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Corresponding author

Correspondence to Pantelis Natsiavas .

Editor information

Editors and Affiliations

  1. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. University of Warwick, Coventry, United Kingdom

    Jonathan Cave

  3. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Anna Satsiou

  4. Technical University Munich, Garching, Germany

    Georg Carle

  5. T6 Ecosystems, Rome, Italy

    Antonella Passani

  6. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Efstratios Kontopoulos

  7. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Sotiris Diplaris

  8. Stockholm University, Stockholm, Sweden

    Donald McMillan

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Natsiavas, P., Maglaveras, N., Koutkias, V. (2017). Evaluation of Linked, Open Data Sources for Mining Adverse Drug Reaction Signals. In: Kompatsiaris, I., et al. Internet Science. INSCI 2017. Lecture Notes in Computer Science(), vol 10673. Springer, Cham. https://doi.org/10.1007/978-3-319-70284-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-70284-1_24

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