Advertisement

Towards a Conceptual Model for Enhancing Reasoning About Clinical Guidelines

A Case-Study on Comorbidity
  • Veruska ZamborliniEmail author
  • Marcos da Silveira
  • Cédric Pruski
  • Annette ten Teije
  • Frank van Harmelen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8903)

Abstract

Computer-Interpretable Guidelines (CIGs) are representations of Clinical Guidelines (CGs) in computer interpretable languages. CIGs have been pointed as an alternative to deal with the various limitations of paper based CGs to support healthcare activities. Although the improvements offered by existing CIG languages, the complexity of the medical domain requires advanced features in order to reuse, share, update, combine or personalize their contents. We propose a conceptual model for representing the content of CGs as a result from an iterative approach that take into account the content of real CGs, CIGs languages and foundational ontologies in order to enhance the reasoning capabilities required to address CIG use-cases. In particular, we apply our approach to the comorbidity use-case and illustrate the model with a realistic case study (Duodenal Ulcer and Transient Ischemic Attack) and compare the results against an existing approach.

Keywords

Duodenal Ulcer Proton Pump Inhibitor Care Action Situation Type Reasoning Capability 
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.

References

  1. 1.
    Bonacin, R., Pruski, C., Da Silveira, M.: Architecture and services for formalising and evaluating care actions from computer-interpretable guidelines. IJMEI Int. J. Med. Eng. Inform. 5, 253–268 (2013)Google Scholar
  2. 2.
    Bottrighi, A., Chesani, F., Mello, P., Montali, M., Montani, S., Terenziani, P.: Conformance checking of executed clinical guidelines in presence of basic medical knowledge. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part II. LNBIP, vol. 100, pp. 200–211. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Boxwala, A.A., Peleg, M., Tu, S.W., Ogunyemi, O., Zeng, Q.T., Wang, D., Patel, V.L., Greenes, R.A., Shortliffe, E.H.: GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J. Biomed. Inform. 37(3), 147–161 (2004)CrossRefGoogle Scholar
  4. 4.
    Guizzardi, G.: Ontological foundations for structural conceptual models. Ph.D. thesis, CTIT, Centre for Telematics and Information Technology, Enschede (2005)Google Scholar
  5. 5.
    Isern, D., Moreno, A.: Computer-based execution of clinical guidelines: a review. Int. J. Med. Inform. 77(12), 787–808 (2008)CrossRefGoogle Scholar
  6. 6.
    Jafarpour, B., Abidi, S.S.R.: Merging disease-specific clinical guidelines to handle comorbidities in a clinical decision support setting. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 28–32. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Latoszek-Berendsen, A., Talmon, J., de Clercq, P., Hasman, A.: With good intentions. Int. J. Med. Inform. 76, S440–S446 (2007)CrossRefGoogle Scholar
  8. 8.
    Shahar, Y., Miksch, S., Johnson, P.: Asbru: a task-specifc, intention-based, and time-oriented language for representing skeletal plans. In: Keravnou, E.T., Baud, R.H., Garbay, C., Wyatt, J.C. (eds.) AIME 1997. LNCS, vol. 1211. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  9. 9.
    Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46(4), 744–763 (2013)CrossRefGoogle Scholar
  10. 10.
    Riano, D.: The SDA model: a set theory approach. In: 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS’07), pp. 563–568. IEEE (2007)Google Scholar
  11. 11.
    Riaño, D., Collado, A.: Model-based combination of treatments for the management of chronic comorbid patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 11–16. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Sánchez-Garzón, I., Fdez-Olivares, J., Onaindía, E., Milla, G., Jordán, J., Castejón, P.: A multi-agent planning approach for the generation of personalized treatment plans of comorbid patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 23–27. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Sutton, D.R., Fox, J.: The syntax and semantics of the PROforma guideline modeling language. J. AMIA 10, 433–443 (2003)Google Scholar
  14. 14.
    Textor, M.: States of affairs. In: Zalta, E. (ed.) The Stanford Encyclopedia of Philosophy, 201 edn. (2012). http://plato.stanford.edu/archives/sum2012/entries/states-of-affairs/
  15. 15.
    Wilk, S., Michalowski, M., Michalowski, W., Hing, M.M., Farion, K.: Reconciling pairs of concurrently used clinical practice guidelines using Constraint Logic Programming. In: AMIA Annual Symposium Proceedings, p. 944. AMIA (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Veruska Zamborlini
    • 1
    • 2
    Email author
  • Marcos da Silveira
    • 2
  • Cédric Pruski
    • 2
  • Annette ten Teije
    • 1
  • Frank van Harmelen
    • 1
  1. 1.VU University AmsterdamAmsterdamThe Netherlands
  2. 2.Public Research Center Henri TudorEsch-sur-AlzetteLuxembourg

Personalised recommendations