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Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD

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Biomedical Engineering Systems and Technologies (BIOSTEC 2016)

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

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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.

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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.

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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.

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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
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  2. Rinke Hoekstra
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  3. Marcos da Silveira
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  4. Cedric Pruski
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  5. Annette ten Teije
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  6. Frank van Harmelen
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Corresponding author

Correspondence to Veruska Zamborlini .

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Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. New University of Lisbon, Lisbon, Portugal

    Hugo Gamboa

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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

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

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