A Common Ontology Based Approach for Clinical Practice Guidelines Using OWL-Ontologies

  • Khalid SamaraEmail author
  • Munir Naveed
  • Yasir Javed
  • Mouza Alshemaili
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


The production and dissemination of clinical practice guidelines (CPG) is usually reliant upon the opinions and interventions of the physicians’ knowledge that are presented in the form of text narratives. The knowledge utilized during the production of CPGs, is largely technical and procedural knowledge. However, the cognitive challenge encountered by the physician is to internalize this new guideline knowledge routinely into actions and clinical decisions. Ontologies have often been used to formalize and represent clinical guidelines. In this study, we propose an approach to the acquisition of CPG knowledge into computer-interpretable form to develop a semantically rich common ontology. To establish a comprehensive representation of CPGs we analyzed abstracts taken from the sub-domains of HeartDiseases related to its diagnosis, possible treatments, and interventions and structured them using the protégé-OWL formal modeling tool. The completeness, and expressiveness of the ontology are then validated using structured and unstructured queries.


  1. 1.
    Klein, G.A., Calderwood, R., Macgregor, D.: Critical decision method for eliciting knowledge. IEEE Trans. Syst. Man Cybern. 19(3), 462–472 (1989)CrossRefGoogle Scholar
  2. 2.
    Nonaka, I.: Dynamic theory of organisational knowledge creation. Organ. Sci. 5(1), 14–37 (1994)CrossRefGoogle Scholar
  3. 3.
    Zhou, L., Nunes, M.B.: Knowledge sharing in healthcare sectors. In: Knowledge Sharing in Chinese Hospitals, pp. 19–38. Springer, Heidelberg (2015)Google Scholar
  4. 4.
    Irby, D.M.: Excellence in clinical teaching: knowledge transformation and development required. Med. Educ. 48(8), 776–784 (2014)CrossRefGoogle Scholar
  5. 5.
    Pourzolfaghar, Z., et al.: A technique to capture multidisciplinary tacit knowledge during the conceptual design phase of a building project. J. Inf. Knowl. Manage. 13(2), 1450013 (2014)CrossRefGoogle Scholar
  6. 6.
    Rizzo, J.: Patients’ mental models and adherence to outpatient physical therapy home exercise programs. Physiother. Theory Pract. 31(4), 253–259 (2015)CrossRefGoogle Scholar
  7. 7.
    Tuan, L.T.: The role of CSR in clinical governance and its influence on knowledge sharing. Clin. Gov. Int. J. 18(2), 90–113 (2013)Google Scholar
  8. 8.
    Straus, S.E., Tetroe, J., Graham, I.: Defining knowledge translation. Can. Med. Assoc. J. 181(3–4), 165–168 (2009)CrossRefGoogle Scholar
  9. 9.
    Kamsu-Foguem, B., Tchuenté-Foguem, G., Foguem, C.: Using conceptual graphs for clinical guidelines representation and knowledge visualization. Inf. Syst. Front. 16(4), 571–589 (2014)CrossRefGoogle Scholar
  10. 10.
    Parry, D.: A fuzzy ontology for medical document retrieval. In: Proceedings of the Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation, vol. 32. Australian Computer Society (2004)Google Scholar
  11. 11.
    Riaño, D., et al.: An ontology-based personalization of healthcare knowledge to support clinical decisions for chronically ill patients. J. Biomed. Inform. 45(3), 429–446 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, H.Q., et al.: Creating personalised clinical pathways by semantic interoperability with electronic health records. Artif. Intell. Med. 58(2), 81–89 (2013)CrossRefGoogle Scholar
  13. 13.
    Topaz, M., et al.: Developing nursing computer interpretable guidelines: a feasibility study of heart failure guidelines in homecare. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association (2013)Google Scholar
  14. 14.
    Resnik, P.: Semantic similarity in taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. AI Res. 11, 95–130 (1998)zbMATHGoogle Scholar
  15. 15.
    McGuinness, D.L., Harmelen, F.V.: OWL web ontology language overview. W3C recommendation, vol. 10(10) (2004)Google Scholar
  16. 16.
    Hameed, A., Preece, A., Sleeman, D.: Ontology reconciliation. In: Handbook on Ontologies, pp. 231–250. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Green, L.A., Seifert, C.M.: Translation of research into practice: why we can’t “just do it”. J. Am. Board Fam. Pract. 18(6), 541–545 (2005)CrossRefGoogle Scholar
  18. 18.
    Santos, J.M., Santos, B.S., Teixeira, L.: Using ontologies and semantic web technology on a clinical pedigree information system. In: Digital Human Modeling. Applications in Health, Safety, Ergonomics and Risk Management, pp. 448–459. Springer, Cham (2014)Google Scholar
  19. 19.
    Jović, A., Gamberger, D., Krstačić, G.: Heart failure ontology. Bio Algorithms Med. Syst. 7(2), 101–110 (2011)Google Scholar
  20. 20.
    Maarouf, H., et al.: An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA). BMC Med. Inf. Decis. Mak. 17(1), 159 (2017)CrossRefGoogle Scholar
  21. 21.
    Lovering, R.C., et al.: Improving interpretation of cardiac phenotypes and enhancing discovery with expanded knowledge in the gene ontology. Circ. Genom. Precis. Med. 11(2), e001813 (2018)CrossRefGoogle Scholar
  22. 22.
    Gruber, T.R., Tenenbaum, J.M., Weber, J.C.: Toward a knowledge medium for collaborative product development. In: Artificial Intelligence in Design 1992, pp. 413–432. Springer, Dordrecht (1992)CrossRefGoogle Scholar
  23. 23.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)CrossRefGoogle Scholar
  24. 24.
    Dueñas, M., et al.: Relationship between using clinical practice guidelines for pain treatment and physicians’ training and attitudes toward patients and the effects on patient care. Pain Pract. 18(1), 38–47 (2018)CrossRefGoogle Scholar
  25. 25.
    Jepsen, T.C.: Just what is an ontology, anyway? IT Prof. Mag. 11(5), 22 (2009)CrossRefGoogle Scholar
  26. 26.
    McMurray, J.J.V., et al.: ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012. Eur. J. Heart Fail. 14(8), 803–869 (2012)CrossRefGoogle Scholar
  27. 27.
    Abu-Hanna, A., et al.: Protégé as a vehicle for developing medical terminological systems. Int. J. Hum. Comput. Stud. 62(5), 639–663 (2005)CrossRefGoogle Scholar
  28. 28.
    El-Sappagh, S., et al.: DMTO: a realistic ontology for standard diabetes mellitus treatment. J. Biomed. Semant. 9(1), 8–16 (2018)CrossRefGoogle Scholar
  29. 29.
    Gorín, D., Meyn, M., Naumann, A., Polzer, M., Rabenstein, U., Schröder, L.: Ontological modelling of a psychiatric clinical practice guideline. In: Joint German/Austrian Conference on Artificial Intelligence. Künstliche Intelligenz, pp. 300–308. Springer, Cham (2017)CrossRefGoogle Scholar
  30. 30.
    Iqtidar, A., et al.: A biomedical ontology on genetic disease. In: Proceedings of the Second International Conference on Internet of Things and Cloud Computing. ACM (2017)Google Scholar
  31. 31.
    Gomez, J., Oviedo, B., Zhuma, E.: Patient monitoring system based on Internet of Things. Procedia Comput. Sci. 83, 90–97 (2016)CrossRefGoogle Scholar
  32. 32.
    Ganzha, M., Paprzycki, M., Pawłowski, W., Szmeja, P., Wasielewska, K.: Semantic interoperability in the Internet of Things: an overview from the INTER-IoT perspective. J. Netw. Comput. Appl. 81, 111–124 (2017)CrossRefGoogle Scholar
  33. 33.
    Bouamrane, M.M., Rector, A., Hurrell, M.: Using OWL ontologies for adaptive patient information modelling and preoperative clinical decision support. Knowl. Inf. Syst. 29(2), 405–418 (2011)CrossRefGoogle Scholar
  34. 34.
    Bouamrane, M.M., Rector, A., Hurrell, M.: Semi-automatic generation of a patient preoperative knowledge-based from a legacy clinical database. In: Proceedings of 8th International Conference on Ontologies, DataBases, and Applications of Semantics, ODBASE 2009, on the Move to Meaningful Internet Systems Conferences, Vilamoura, Algarve, Portugal. LNCS, vol. 5871, pp. 1224–1237. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  35. 35.
    Ramani, G.V., et al.: Chronic heart failure: contemporary diagnosis and management. Mayo Clin. Proc. 85(2), 180–195 (2010)CrossRefGoogle Scholar
  36. 36.
    Figueroa, M.S., Peters, J.I.: Congestive heart failure: diagnosis, pathophysiology, therapy, and implications for respiratory care. Respir. Care 51(4), 403–412 (2006)Google Scholar
  37. 37.
    Guyatt, G.H., Devereaux, P.J.: A review of heart failure treatment. Mt. Sinai J. Med. 71(1), 47–54 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Khalid Samara
    • 1
    Email author
  • Munir Naveed
    • 2
  • Yasir Javed
    • 2
  • Mouza Alshemaili
    • 1
  1. 1.Computer and Information SciencesHigher College of TechnologyRas Al KhaimahUAE
  2. 2.Computer and Information SciencesHigher College of TechnologyAl’AinUAE

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