Advertisement

An Ontological Model for Analyzing Liver Cancer Medical Reports

  • Rim MessaoudiEmail author
  • Taher Labidi
  • Antoine Vacavant
  • Faiez Gargouri
  • Manuel Grand-Brochier
  • Ali Amouri
  • Hela Fourati
  • Achraf Mtibaa
  • Faouzi Jaziri
Conference paper
Part of the Lecture Notes in Business Information Processing book series (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.

Keywords

Ontology MRI reports Liver cancer Reasoning rules 

Notes

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.

References

  1. 1.
    Alfonse, M., Aref, M., Salem, A.B.M.: Ontology-based knowledge representation for liver cancer. In: International eHealth: Telemedicine and Health ICT Forum for Educational, Networking and Business, pp. 821–825 (2012)Google Scholar
  2. 2.
    Abdi, A., Idris, N., Ahmad, Z.: QAPD: an ontology based question answering system in the physics domain. Soft. Comput. 24, 1–18 (2016)Google Scholar
  3. 3.
    Bertolaso, M., Ratti, E.: Conceptual challenges in the theoretical foundations of systems biology. In: Bizzarri, M. (ed.) Systems Biology. MMB, vol. 1702, pp. 1–13. Springer, New York (2018).  https://doi.org/10.1007/978-1-4939-7456-6_1CrossRefGoogle Scholar
  4. 4.
    Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 37, 267–270 (2004)CrossRefGoogle Scholar
  5. 5.
    Chan, L., et al.: Association patterns of ontological features signify electronic health records in liver cancer. J. Healthc. Eng. 2017, 9 (2017)CrossRefGoogle Scholar
  6. 6.
    Chapman, W., Chu, D., Dowling, J.: Context: an algorithm for identifying contextual features from clinical text. In: BioNLP (2007)Google Scholar
  7. 7.
    Darby, S., et al.: Mortality from liver cancer and liver disease in haemophilic men and boys in UK given blood products contaminated with hepatitis C. Lancet 350, 1425–1431 (1997). UK Haemophilia Centre Directors’ OrganisationCrossRefGoogle Scholar
  8. 8.
    Gao, W., Baig, A., Ali, H., Sajjad, W., Farahanic, M.: Margin based ontology sparse vector learning algorithm and applied in biology science. Saudi J. Biol. Sci. 24, 132–138 (2017)CrossRefGoogle Scholar
  9. 9.
    Haacke, E., Brown, R., Thompson, M., Venkatesan, R.: Magnetic Resonance Imaging: Principles and Sequence Design. Wiley, Hoboken (2014)Google Scholar
  10. 10.
    Hahn, U., Romacker, M., Schulz, S.: MEDSYNDIKATE-a natural language system for the extraction of medical information from findings reports. Int. J. Med. Inf. 67, 63–74 (2002)CrossRefGoogle Scholar
  11. 11.
    Hoerbst, A., Ammenwerth, E.: Electronic health records. A systematic review on quality requirements. Methods Inf. Med. 49, 320–336 (2010)CrossRefGoogle Scholar
  12. 12.
    Häyrinen, K., Saranto, K., Nykänen, P.: Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int. J. Med. Inform. 77, 291–304 (2008)CrossRefGoogle Scholar
  13. 13.
    Jensen, P., Jensen, L., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395–407 (2012)CrossRefGoogle Scholar
  14. 14.
    Kokciyan, N., Turkay, R., Uskudarli, S., Yolum, P., Bakir, B., Acar, B.: Semantic description of liver CT images: an ontological approach. IEEE J. Biomed. Health Inform. 18, 1363–1369 (2014)CrossRefGoogle Scholar
  15. 15.
    Labidi, T., Mtibaa, A., Brabra, H.: CSLAOnto: a comprehensive ontological SLA model in cloud computing. J. Data Semant. 5, 179–193 (2016)CrossRefGoogle Scholar
  16. 16.
    Labidi, T., Mtibaa, A., Gaaloul, W., Tata, S., Gargouri., F.: Cloud SLA modeling and monitoring. In: IEEE International Conference on Services Computing (SCC), pp. 338–345 (2017)Google Scholar
  17. 17.
    Marwede, D., Fielding, M., Kahn, T.: Radio: a prototype application ontology for radiology reporting tasks. In: AMIA Symposium Proceedings, vol. 37, pp. 513-517 (2007)Google Scholar
  18. 18.
    Ben Salem, Y., Idoudi, R., Saheb Ettabaa, K., Hamrouni, K., Solaiman, B.: Ontology based possibilistic reasoning for breast cancer aided diagnosis. In: Themistocleous, M., Morabito, V. (eds.) EMCIS 2017. LNBIP, vol. 299, pp. 353–366. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65930-5_29CrossRefGoogle Scholar
  19. 19.
    Spackman, K., Campbell, K., Côté, R.: SNOMED RT: a reference terminology for health care. In: Proceedings of the AMIA Annual Fall Symposium, pp. 640–644 (1997)Google Scholar
  20. 20.
    Starlinger, J., Kittner, M., Blankenstein, O., Leser, U.: How to improve information extraction from German medical records. IT - Inf. Technol. 59, 171–179 (2016)Google Scholar
  21. 21.
    Wang, Y., et al.: Clinical information extraction applications: a literature review. J. Biomed. Inform. 77, 34–49 (2017)CrossRefGoogle Scholar
  22. 22.
    Xu, H., Stenner, S., Doan, S., Johnson, K., Waitman, L., Denny, J.: MedEx: a medication information extraction system for clinical narratives. J. Am. Med. Inform. Assoc. 17, 19–24 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rim Messaoudi
    • 1
    • 2
    Email author
  • Taher Labidi
    • 1
    • 4
  • Antoine Vacavant
    • 3
  • Faiez Gargouri
    • 1
    • 6
  • Manuel Grand-Brochier
    • 3
  • Ali Amouri
    • 5
  • Hela Fourati
    • 5
  • Achraf Mtibaa
    • 1
    • 4
  • Faouzi Jaziri
    • 3
  1. 1.MIRACL LaboratoryUniversity of SfaxSfaxTunisia
  2. 2.CRNS LaboratoryUniversity of SfaxSfaxTunisia
  3. 3.Institut PascalUniversité Clermont AuvergneClermont-FerrandFrance
  4. 4.National School of Electronic and TelecommunicationsUniversity of SfaxSfaxTunisia
  5. 5.CHU Hédi ChakerSfaxTunisia
  6. 6.Higher Institute of Computer Science and MultimediaUniversity of SfaxSfaxTunisia

Personalised recommendations