Advertisement

Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD

  • Veruska Zamborlini
  • Rinke Hoekstra
  • Marcos da Silveira
  • Cedric Pruski
  • Annette ten Teije
  • Frank van Harmelen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 690)

Abstract

This paper presents a method for formally representing Computer-Interpretable Guidelines. It allows for combining them with knowledge from several sources to better detect potential interactions within multimorbidity cases, coping with possibly conflicting pieces of evidence coming from clinical studies. The originality of our approach is on the capacity to analyse combinations of more than two recommendations, which is useful, for instance, for polypharmacy interactions cases. We defined general models to express evidence as causation beliefs and designed general rules for detecting interactions (e.g., conflicts, alternatives, etc.) enriched with Linked Open Data (e.g. Drugbank, Sider). In particular we show that Linked Open Data sources enable us to detect (suspected) interactions among multiple drugs due to polypharmacy. We evaluate our approach in a scenario where three different clinical guidelines (Osteoarthritis, Diabetes, and Hypertension) are combined. We demonstrate the capability of this approach for detecting several potential conflicts between the recommendations and find alternatives.

Keywords

Clinical guidelines Semantic Web Knowledge representation Ontologies 

Notes

Acknowledgments

We would like to thank colleagues from NEMO-UFES/Brazil for fruitful discussions about transitions, causation beliefs and regulations, and also prof. md. Saulo Bortolon for the nice discussions about medical domain; Jan Wielemaker and Wouter Beek (VU Amsterdam) for helping with SWI-Prolog implementation; Wytze Vliestra (Erasmus Rotterdam) for fruitful discussions about the biomedical domain; and Paul Groth (Elsevier) for fruitful discussions about the potential generality of the model and the use of nanopublications. The first author is funded by CNPq (Brazilian National Council for Scientific and Technological Development) within the program Science without Borders. This work was partially funded by the Dutch National Programme COMMIT.

References

  1. 1.
    Zamborlini, V., Hoekstra, R., Silveira, M., Pruski, C., Teije, A.: Generalizing the detection of internal and external interactions in clinical guidelines. In: Proceedings of the 9th International Conference on Health Informatics (HEALTHINF2016), Rome, Italy (2016)Google Scholar
  2. 2.
    Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Informatics 46, 744–763 (2013)CrossRefGoogle Scholar
  3. 3.
    Lohr, K.N.: Rating the strength of scientific evidence: relevance for quality improvement programs. Int. J. Qual. Health Care 16, 9–18 (2003)CrossRefGoogle Scholar
  4. 4.
    Barnett, K., Mercer, S., Norbury, M., Watt, G.: Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. The Lancet (2012)Google Scholar
  5. 5.
    Guthrie, B., Makubate, B., Hernandez-Santiago, V., Dreischulte, T.: The rising tide of polypharmacy and drug-drug interactions: population database analysis 19952010. BMC Med. 13, 74 (2015)CrossRefGoogle Scholar
  6. 6.
    Zamborlini, V., Hoekstra, R., da Silveira, M., Pruski, C., ten Teije, A., van Harmelen, F.: Inferring recommendation interactions in clinical guidelines: case-studies on multimorbidity. Seman. Web J., Open Acess (2015, accepted)Google Scholar
  7. 7.
    Zamborlini, V., Silveira, M., Pruski, C., Teije, A., Harmelen, F.: Towards a conceptual model for enhancing reasoning about clinical guidelines. In: Miksch, S., Riaño, D., Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 29–44. Springer, Cham (2014). doi: 10.1007/978-3-319-13281-5_3 Google Scholar
  8. 8.
    Zamborlini, V., Hoekstra, R., Silveira, M., Pruski, C., Teije, A., Harmelen, F.: A conceptual model for detecting interactions among medical recommendations in clinical guidelines. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 591–606. Springer, Cham (2014). doi: 10.1007/978-3-319-13704-9_44 Google Scholar
  9. 9.
    Jafarpour, B.: Ontology merging using semantically-defined merge criteria and owl reasoning services: towards execution-time merging of multiple clinical workflows to handle comorbidity. Ph.D. thesis, Dalhousie University (2013)Google Scholar
  10. 10.
    Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., MacIejewski, A., Arndt, D., Wilson, M., Neveu, V., Tang, A., Gabriel, G., Ly, C., Adamjee, S., Dame, Z.T., Han, B., Zhou, Y., Wishart, D.S.: DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, 1091–1097 (2014). D1091–7, PubMed ID: 24203711CrossRefGoogle Scholar
  11. 11.
    Kuhn, M., Letunic, I., Jensen, L.J., Bork, P.: The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075–D1079 (2016)CrossRefGoogle Scholar
  12. 12.
    Boyce, R., Collins, C., Horn, J., Kalet, I.: Computing with evidence part I: a drug-mechanism evidence taxonomy oriented toward confidence assignment. J. Biomed. Inform. 42, 979–989 (2009)CrossRefGoogle Scholar
  13. 13.
    Banda, J.M., Kuhn, T., Shah, N.H., Dumontier, M.: Provenance-centered dataset of drug-drug interactions. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 293–300. Springer, Cham (2015). doi: 10.1007/978-3-319-25010-6_18
  14. 14.
    Hoekstra, R., Magliacane, S., Rietveld, L., Vries, G., Wibisono, A., Schlobach, S.: Hubble: linked data hub for clinical decision support. In: Simperl, E., Norton, B., Mladenic, D., Della Valle, E., Fundulaki, I., Passant, A., Troncy, R. (eds.) ESWC 2012. LNCS, vol. 7540, pp. 458–462. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-46641-4_45 Google Scholar
  15. 15.
    Zamborlini, V., Silveira, M., Pruski, C., Teije, A., Harmelen, F.: Analyzing recommendations interactions in clinical guidelines. In: Holmes, J.H., Bellazzi, R., Sacchi, L., Peek, N. (eds.) AIME 2015. LNCS (LNAI), vol. 9105, pp. 317–326. Springer, Cham (2015). doi: 10.1007/978-3-319-19551-3_40 CrossRefGoogle Scholar
  16. 16.
    Guizzardi, G., Wagner, G., Almeida Falbo, R., Guizzardi, R.S.S., Almeida, J.P.A.: Towards ontological foundations for the conceptual modeling of events. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) ER 2013. LNCS, vol. 8217, pp. 327–341. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41924-9_27 CrossRefGoogle Scholar
  17. 17.
    ten Teije, A., Miksch, S., Lucas, P. (eds.): Computer-Based Medical Guidelines and Protocols: A Primer and Current Trends. Technology and Informatics, vol. 139 (2008)Google Scholar
  18. 18.
    Ammenwerth, E., Schnell-Inderst, P., Machan, C., Siebert, U.: The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J. Am. Med. Inform. Assoc. 15, 585–600 (2008)CrossRefGoogle Scholar
  19. 19.
    López-Vallverdú, J.A., Riaño, D., Collado, A.: Rule-based combination of comorbid treatments for chronic diseases applied to hypertension, diabetes mellitus and heart failure. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., Teije, A. (eds.) KR4HC/ProHealth -2012. LNCS (LNAI), vol. 7738, pp. 30–41. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36438-9_2 CrossRefGoogle Scholar
  20. 20.
    Wilk, S., Michalowski, M., Tan, X., Michalowski, W.: Using first-order logic to represent clinical practice guidelines and to mitigate adverse interactions. In: Miksch, S., Riaño, D., Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 45–61. Springer, Cham (2014). doi: 10.1007/978-3-319-13281-5_4 Google Scholar
  21. 21.
    Piovesan, L., Molino, G., Terenziani, P.: An ontological knowledge and multiple abstraction level decision support system in healthcare. Decis. Anal. 1, 8 (2014)CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    de Waard, A., Shum, S.B., Carusi, A., Park, J., Samwald, M., Sándor, Á.: Hypotheses, evidence and relationships: the hyper approach for representing scientific knowledge claims. In: Proceedings of the 8th ISWC, Workshop on Semantic Web Applications in Scientific Discourse. Springer, Berlin (2009)Google Scholar
  24. 24.
    Hoekstra, R., de Waard, A., Vdovjak, R.: Annotating evidence based clinical guidelines - a lightweight ontology. In: Paschke, A., Burger, A., Romano, P., Marshall, M.S., Splendiani, A. (eds.) Proceedings of the 5th International Workshop on Semantic Web Applications and Tools for Life Sciences, Paris, France, 28–30 November 2012. CEUR Workshop Proceedings, vol. 952 (2012). CEUR-WS.org
  25. 25.
    Huang, Z., Teije, A., Harmelen, F., Aït-Mokhtar, S.: Semantic representation of evidence-based clinical guidelines. In: Miksch, S., Riaño, D., Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 78–94. Springer, Cham (2014). doi: 10.1007/978-3-319-13281-5_6 Google Scholar
  26. 26.
    Mons, B., van Haagen, H., Chichester, C., Hoen, P.B., den Dunnen, J., van Ommen, G., van Mulligen, E., Singh, B., Hooft, R., Roos, M., Hammond, J., Kiesel, B., Giardine, B., Velterop, J., Groth, P., Schultes, E.: The value of data. Nat. Genet. 43, 281–283 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Veruska Zamborlini
    • 1
    • 3
  • Rinke Hoekstra
    • 1
    • 2
  • Marcos da Silveira
    • 3
  • Cedric Pruski
    • 3
  • Annette ten Teije
    • 1
  • Frank van Harmelen
    • 1
  1. 1.Department of Computer ScienceVU University AmsterdamAmsterdamThe Netherlands
  2. 2.Faculty of LawUniversity of AmsterdamAmsterdamThe Netherlands
  3. 3.Luxembourg Institute of Science and Technology - LISTEsch-sur-AlzetteLuxembourg

Personalised recommendations