Advertisement

Semi-automatic Generation of a Patient Preoperative Knowledge-Base from a Legacy Clinical Database

  • Matt-Mouley Bouamrane
  • Alan Rector
  • Martin Hurrell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5871)

Abstract

We discuss our practical experience of automating the process of migrating a clinical database with a weak underlying information model towards a high level representation of a patient medical history information in the Web Ontology Language (OWL). The purpose of this migration is to enable sophisticated clinical decision support functionalities based on semantic-web technologies, i.e. reasoning on a clinical ontology. We discuss the research and practical motivation behind this process, including improved interoperability and additional classification functionalities. We propose a methodology to optimise the efficiency of this process and provide practical implementation examples.

Keywords

Database Schema Clinical Information System High Level Representation Rule Engine Implicit Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bouamrane, M.M., Rector, A., Hurrell, M.: Gathering Precise Patient Medical History with an Ontology-driven Adaptive Questionnaire. In: Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008, Jyväskylä, Finland, pp. 539–541. IEEE Computer Society, Los Alamitos (2008)CrossRefGoogle Scholar
  2. 2.
    Bouamrane, M.-M., Rector, A.L., Hurrell, M.: Ontology-Driven Adaptive Medical Information Collection System. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 574–584. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Bouamrane, M.M., Rector, A.L., Hurrell, M.: Using Ontologies for an Intelligent Patient Modelling, Adaptation and Management System. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1458–1470. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    Bouamrane, M.M., Rector, A., Hurrell, M.: Development of an Ontology of Preoperative Risk Assessment for a Clinical Decision Support System. In: Proceedings of the 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009, Albuquerque, US. IEEE Computer Society, Los Alamitos (to appear, 2009)Google Scholar
  5. 5.
    Copeland, G., Jones, D., Walters, M.: Possum: a scoring system for surgical audit. British Journal of Surgery 78(3), 355–360 (1991)CrossRefGoogle Scholar
  6. 6.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. Journal of Web Semantic 5(2), 51–53 (2007)Google Scholar
  7. 7.
    Brachman, R., Levesque, H.: Knowledge Representation and Reasoning. Morgan Kaufmann, Elsevier (2004)Google Scholar
  8. 8.
    Stojanovic, N., Stojanovic, L., Volz, R.: A reverse engineering approach for migrating data-intensive web sites to the semantic web. In: Proceedings of Intelligent Information Processing, IFIP 17th World Computer Congress, Montral, Qubec, Canada, August 2002, pp. 141–154 (2002)Google Scholar
  9. 9.
    Gottgtroy, P., Kasabov, N., MacDonell, S.: An ontology engineering approach for knowledge discovery from data in evolving domains. In: Proceedings of Data Mining IV, Rio de Janeiro, Brasil, pp. 43–52 (2003)Google Scholar
  10. 10.
    Verheyden, P., De Bo, J., Meersman, R.: Semantically unlocking database content through ontology-based mediation. In: Bussler, C.J., Tannen, V., Fundulaki, I. (eds.) SWDB 2004. LNCS, vol. 3372, pp. 109–126. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Daga, A., de Cesare, S., Lycett, M., Partridge, C.: An ontological approach for recovering legacy business content. In: Hawaii International Conference on System Sciences, vol. 8, p. 224a (2005)Google Scholar
  12. 12.
    Kupfer, A., Eckstein, S., Neumann, K., Mathiak, B.: Handling changes of database schemas and corresponding ontologies. In: Roddick, J., Benjamins, V.R., Si-said Cherfi, S., Chiang, R., Claramunt, C., Elmasri, R.A., Grandi, F., Han, H., Hepp, M., Lytras, M.D., Mišić, V.B., Poels, G., Song, I.-Y., Trujillo, J., Vangenot, C. (eds.) ER Workshops 2006. LNCS, vol. 4231, pp. 227–236. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Benslimane, S.M., Malki, M., Rahmouni, M.K., Benslimane, D.: Extracting Personalised Ontology from Data-Intensive Web Application: an HTML Forms-Based Reverse Engineering Approach. Informatica 18(4), 511–534 (2007)zbMATHGoogle Scholar
  14. 14.
    Lubyte, L., Tessaris, S.: Extracting ontologies from relational databases. In: Proceedings of the 20th Int. Workshop on Description Logics, DL 2007, Brixen-Bressanone, Italy, pp. 387–395 (2007)Google Scholar
  15. 15.
    Cure, O., Bensaid, J.D.: Integration of relational databases into OWL knowledge bases: demonstration of the DBOM system. In: Proceedings of the 24th International Conference on Data Engineering Workshops, ICDE 2008, Cancn, Mxico, April 2008, pp. 230–233. IEEE Computer Society, Los Alamitos (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Matt-Mouley Bouamrane
    • 1
    • 2
  • Alan Rector
    • 1
  • Martin Hurrell
    • 2
  1. 1.School of Computer ScienceManchester UniversityUK
  2. 2.CIS InformaticsGlasgowUK

Personalised recommendations