Skip to main content
SpringerLink
Log in

Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD

  • Conference paper
  • First Online:
Biomedical Engineering Systems and Technologies (BIOSTEC 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 690))

  • 663 Accesses

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.

Invited submission as extension of [1].

V. Zamborlini—Funded by CNPq (Brazilian National Council for Scientific and Technological Development) within the Science without Borders programme.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://drugbank.ca/.

  2. 2.

    http://sideeffects.embl.de.

  3. 3.

    http://dbmi-icode-01.dbmi.pitt.edu/dikb-evidence/front-page.html.

  4. 4.

    https://datahub.io/dataset/linked-drug-drug-interactions-liddi.

  5. 5.

    http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/.

  6. 6.

    For sake of simplicity we can omit the word ‘type’.

  7. 7.

    For a deeper explanation see [7].

  8. 8.

    Detailed discussion about (non-)deterministic or (non-)intentional event types is out of scope of this work.

  9. 9.

    This approach exclude endless assertions about all the effects an event is not expected to produce since the beliefs are defined in CGs or scientific papers by a community of experts, e.g. cancer is not an effect of a certain drug.

  10. 10.

    Accessible at http://rapgmsbgym.github.io.

  11. 11.

    The Drug and Situation Types are mirrored and mapped to the to the external knowledge sources via owl:sameAs.

  12. 12.

    http://rapgmsbgym.github.io.

  13. 13.

    See http://guidelines.hoekstra.ops.few.vu.nl.

  14. 14.

    See http://www.openannotation.org.

  15. 15.

    See http://www.w3.org/TR/prov-o.

References

  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. Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Informatics 46, 744–763 (2013)

    Article  Google Scholar 

  3. Lohr, K.N.: Rating the strength of scientific evidence: relevance for quality improvement programs. Int. J. Qual. Health Care 16, 9–18 (2003)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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: 24203711

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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. Piovesan, L., Molino, G., Terenziani, P.: An ontological knowledge and multiple abstraction level decision support system in healthcare. Decis. Anal. 1, 8 (2014)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Computer Science, VU University Amsterdam, Amsterdam, The Netherlands

    Veruska Zamborlini, Rinke Hoekstra, Annette ten Teije & Frank van Harmelen

  2. Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands

    Rinke Hoekstra

  3. Luxembourg Institute of Science and Technology - LIST, Esch-sur-Alzette, Luxembourg

    Veruska Zamborlini, Marcos da Silveira & Cedric Pruski

Authors
  1. Veruska Zamborlini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rinke Hoekstra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcos da Silveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cedric Pruski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Annette ten Teije
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Frank van Harmelen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Veruska Zamborlini .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. New University of Lisbon, Lisbon, Portugal

    Hugo Gamboa

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zamborlini, V., Hoekstra, R., da Silveira, M., Pruski, C., ten Teije, A., van Harmelen, F. (2017). Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD. In: Fred, A., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2016. Communications in Computer and Information Science, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-54717-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54717-6_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54716-9

  • Online ISBN: 978-3-319-54717-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation