Advertisement

Using Semantic Web Technologies to Underpin the SNOMED CT Query Language

  • Mercedes Arguello Casteleiro
  • Dmitry Tsarkov
  • Bijan Parsia
  • Ulrike Sattler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10630)

Abstract

SNOMED International is working on a query language specification for SNOMED CT, which we call here SCTQL. SNOMED CT is the leading terminology for use in Electronic Health Records (EHRs). SCTQL can contribute to effective retrieval and reuse of clinical information within EHRs. This paper analyses the functional capabilities needed for SCTQL and proposes two implementations that rely on ontological representations of SNOMED CT: one based on the W3C SPARQL 1.1 query language and another based on the OWL API. The paper reports the performance and correctness of both implementations as well as highlights their benefits and drawbacks.

Keywords

SNOMED CT Reference sets Ontology SPARQL OWL API 

References

  1. 1.
    SNOMED International. http://www.snomed.org
  2. 2.
    Wardle, M., Spencer, A.: Implementation of SNOMED CT in an online clinical database. Future Hosp. J. 4, 126–130 (2017)Google Scholar
  3. 3.
    NLM Tools for EHR Certification and Meaningful Use. http://www.nlm.nih.gov/healthit/meaningful_use.html
  4. 4.
    Lee, D., Cornet, R., Lau, F., de Keizer, N.: A survey of SNOMED CT implementations. J. Biomed. Inform. 46, 87–96 (2013)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Lee, D.H., Lau, F.Y., Quan, H.: A method for encoding clinical datasets with SNOMED CT. BMC Med. Inf. Decis. Making 10, 53 (2010)CrossRefGoogle Scholar
  7. 7.
    Clinical Observations Recording and Encoding (CORE) Problem List Subset of SNOMED CT. http://www.nlm.nih.gov/research/umls/Snomed/core_subset.html
  8. 8.
  9. 9.
    Veterans Health Administration and Kaiser Permanente (VA/KP) Problem List subset. http://www.nlm.nih.gov/research/umls/Snomed/snomed_problem_list.html
  10. 10.
  11. 11.
    Hansen, D.P., Giermanski, M., Dujmovic, M., Passenger, J., Lawley, M.J.: Building SNOMED CT reference sets for use as interface terminologies. Electron. J. Health Inf. 6, 1 (2011)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Data Analytics with SNOMED CT. http://snomed.org/analytics
  14. 14.
    Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014)CrossRefGoogle Scholar
  15. 15.
  16. 16.
  17. 17.
    SNOMED CT Query Language Specification version 0.8 draft. http://www.cs.man.ac.uk/~rector/temp/SNOMED_TQL_for_comment.doc
  18. 18.
  19. 19.
  20. 20.
    SPARQL 1.1 query language. http://www.w3.org/TR/sparql11-query/
  21. 21.
    Horridge, M., Bechhofer, S.: The OWL API: A Java API for OWL ontologies. Seman. Web 2, 11–21 (2011)Google Scholar
  22. 22.
    Schulz, S., Jansen, L.: Formal ontologies in biomedical knowledge representation. Yearb Med Inform 8, 132–146 (2013)Google Scholar
  23. 23.
    Baader, F., Horrocks, I., Sattler, U.: Description logics as ontology languages for the semantic web. In: Hutter, D., Stephan, W. (eds.) Mechanizing Mathematical Reasoning. LNCS, vol. 2605, pp. 228–248. Springer, Heidelberg (2005).  https://doi.org/10.1007/978-3-540-32254-2_14 CrossRefGoogle Scholar
  24. 24.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284, 28–37 (2001)CrossRefGoogle Scholar
  25. 25.
    Antoniou, G., Van Harmelen, F.: A Semantic Web Primer. MIT Press, Cambridge (2004)Google Scholar
  26. 26.
    Baader, F., Horrocks, I., Lutz, C., Sattler, U.: An Introduction to Description Logic. Cambridge University Press, Cambridge (2017)CrossRefzbMATHGoogle Scholar
  27. 27.
    Krötzsch, M.: OWL 2 profiles: an introduction to lightweight ontology languages. In: Eiter, T., Krennwallner, T. (eds.) Reasoning Web 2012. LNCS, vol. 7487, pp. 112–183. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33158-9_4 CrossRefGoogle Scholar
  28. 28.
    Wang, Y., Halper, M., Wei, D., Gu, H., Perl, Y., Xu, J., Elhanan, G., Chen, Y., Spackman, K.A., Case, J.T., Hripcsak, G.: Auditing complex concepts of SNOMED using a refined hierarchical abstraction network. J. Biomed. Inform. 45, 1–14 (2012)CrossRefGoogle Scholar
  29. 29.
    Jiang, G., Chute, C.G.: Auditing the semantic completeness of SNOMED CT using formal concept analysis. J. Am. Med. Inform. Assoc. 16, 89–102 (2009)CrossRefGoogle Scholar
  30. 30.
    Horridge, M., Drummond, N., Goodwin, J., Rector, A.L., Stevens, R., Wang, H.: The manchester OWL syntax. In: OWLed, vol. 216 (2006)Google Scholar
  31. 31.
    Hartel, F.W., de Coronado, S., Dionne, R., Fragoso, G., Golbeck, J.: Modeling a description logic vocabulary for cancer research. J. Biomed. Inform. 38, 114–129 (2005)CrossRefGoogle Scholar
  32. 32.
  33. 33.
    SPARQL 1.1 Entailment Regimes. http://www.w3.org/TR/sparql11-entailment/
  34. 34.
    Zhang, G.-Q., Bodenreider, O.: Using SPARQL to test for lattices: application to quality assurance in biomedical ontologies. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 273–288. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-17749-1_18 CrossRefGoogle Scholar
  35. 35.
  36. 36.
  37. 37.
    Kim, J.D., Cohen, K.B.: Natural language query processing for SPARQL generation: a prototype system for SNOMED CT. In: BioLINK, pp. 32–38 (2013)Google Scholar
  38. 38.
    Alonso-Calvo, R., Paraiso-Medina, S., Perez-Rey, D., Alonso-Oset, E., van Stiphout, R., Yu, S., Taylor, M., Buffa, F., Fernandez-Lozano, C., Pazos, A., Maojo, V.: A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer. Comput. Biol. Med. (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mercedes Arguello Casteleiro
    • 1
  • Dmitry Tsarkov
    • 1
  • Bijan Parsia
    • 1
  • Ulrike Sattler
    • 1
  1. 1.School of Computer ScienceThe University of ManchesterManchesterUK

Personalised recommendations