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An Ontological Model for Analyzing Liver Cancer Medical Reports

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Book cover Information Systems (EMCIS 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 341))

Abstract

The rapid adoption of Electronic Health Record (EHR) systems requires advanced enactment strategies for analyzing medical reports. Indeed, the information presented in these reports is difficult to access and it is onerous to analyze it by medical decision support systems. Medical reports characterize full descriptions of the patient diagnosis process. They bring together information about exam steps such as applied techniques, results, synthesis and medical conclusions. In this paper, we propose a medical report modeling and analyzing approach that aims to analyze medical reports for Magnetic Resonance Imaging (MRI) exams. Ontological model is dedicated to represent information from radiological reports in order to make them comprehensible and machine readable. Moreover, reasoning techniques are used to treat a large amount of clinical data. This provides an analyzing system allowing user to be informed about the evolution of the patient state. The proposed system was successfully applied to a set of Hepatocellular Carcinoma (HCC) medical reports from University Hospital of Clermont-Ferrand (CHU), France.

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Notes

  1. 1.

    CIRRHOSE project (Classification d’Images Rpartie pour la Reconnaissance de lsions Hpatiques, avec des OntologieS, et grande Echelle). It focuses on liver tumors diagnosis and treatment and it takes into account in particular HCC diseases.

  2. 2.

    https://protegewiki.stanford.edu/wiki/OntoGraf.

  3. 3.

    https://protege.stanford.edu/.

  4. 4.

    https://www.w3.org/.

  5. 5.

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

  6. 6.

    https://github.com/protegeproject/swrlapi/wiki/SQWRL.

  7. 7.

    https://www.eclipse.org/modeling/emf/.

  8. 8.

    https://jena.apache.org/.

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Acknowledgements

This work was financially supported by the “PHC Utique” program of the French Ministry of Foreign A airs and Ministry of higher education and research and the Tunisian Ministry of higher education and scientific research in the CMCU project number 18G139.

Author information

Authors and Affiliations

  1. MIRACL Laboratory, University of Sfax, Sfax, Tunisia

    Rim Messaoudi, Taher Labidi, Faiez Gargouri & Achraf Mtibaa

  2. CRNS Laboratory, University of Sfax, Sfax, Tunisia

    Rim Messaoudi

  3. Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, France

    Antoine Vacavant, Manuel Grand-Brochier & Faouzi Jaziri

  4. National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia

    Taher Labidi & Achraf Mtibaa

  5. CHU Hédi Chaker, Sfax, Tunisia

    Ali Amouri & Hela Fourati

  6. Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia

    Faiez Gargouri

Authors
  1. Rim Messaoudi
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  2. Taher Labidi
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  3. Antoine Vacavant
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  4. Faiez Gargouri
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  5. Manuel Grand-Brochier
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  6. Ali Amouri
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  7. Hela Fourati
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  8. Achraf Mtibaa
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  9. Faouzi Jaziri
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Corresponding author

Correspondence to Rim Messaoudi .

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Editors and Affiliations

  1. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. University of Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

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Messaoudi, R. et al. (2019). An Ontological Model for Analyzing Liver Cancer Medical Reports. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_29

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  • DOI: https://doi.org/10.1007/978-3-030-11395-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11394-0

  • Online ISBN: 978-3-030-11395-7

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