Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

Veruska Zamborlini, Rinke Hoekstra, Marcos da Silveira, Cedric Pruski, Annette ten Teije, Frank van Harmelen

2016

Abstract

This paper presents a method for formally representing Computer-Interpretable Guidelines to deal with multimorbidity. Although some approaches for merging guidelines exist, improvements are still required for combining several sources of information and coping with possibly conflicting pieces of evidence coming from clinical studies. Our main contribution is twofold: (i) we provide general models and rules for representing guidelines that expresses evidence as causation beliefs; (ii) we introduce a mechanism to exploit external medical knowledge acquired from Linked Open Data (Drugbank, Sider, DIKB) to detect potential interactions between recommendations. We apply this framework to merge three guidelines (Osteoarthritis, Diabetes, and Hypertension) in order to illustrate the capability of this approach for detecting potential conflicts between guidelines and eventually propose alternatives.

References

  1. Ammenwerth, E., Schnell-Inderst, P., Machan, C., and Siebert, U. (2008). The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. Journal of the American Medical Informatics Association, 15(5):585-600.
  2. Annette ten Teije, Silvia Miksch, P. L., editor (2008). Computer-based Medical Guidelines and Protocols: A Primer and Current Trends, volume 139 of Technology and Informatics.
  3. Barnett, K., Mercer, S., Norbury, M., and Watt, G. (2012). Epidemiology of multimorbidity and implications for health care, research, and medical education: a crosssectional study. The Lancet.
  4. Bonacin, R., Pruski, C., and Da Silveira, M. (2013). Architecture and services for formalising and evaluating care actions from computer-interpretable guidelines. IJMEI International Journal of Medical Engineering and Informatics, 5:253-268.
  5. de Waard, A., Shum, S. B., Carusi, A., Park, J., Samwald, M., and Sándor, Í. (2009). Hypotheses, evidence and relationships: The hyper approach for representing scientific knowledge claims. InProceedings of the 8th ISWC, Workshop on Semantic Web Applications in Scientific Discourse , Berlin. Springer.
  6. Guizzardi, G., Wagner, G., de Almeida Falbo, R., Guizzardi, R. S. S., and Almeida, J. P. A. (2013). Towards Ontological Foundations for the Conceptual Modeling of Events. In Conceptual Modeling, 32th International Conference, ER 2013, pages 327-341, HongKong. Springer Berlin Heidelberg.
  7. Hoekstra, R., de Waard, A., and Vdovjak, R. (2012). Annotating evidence based clinical guidelines - A lightweight ontology. In Paschke, A., Burger, A., Romano, P., Marshall, M. S., and Splendiani, A., editors, Proceedings of the 5th International Workshop on Semantic Web Applications and Tools for Life Sciences, Paris, France, November 28-30, 2012, volume 952 of CEUR Workshop Proceedings. CEUR-WS.org.
  8. Huang, Z., ten Teije, A., van Harmelen, F., and AitMokhtar, S. (2014). Semantic Representation of Evidence-based Clinical Guidelines. In 6th International Workshop on Knowledge Representation for Health Care (KR4HC2014), volume 8903 of LNCS.
  9. Jafarpour, B. (2013). Ontology Merging using Semantically-defined Merge Criteria and OWL Reasoning Services: Towards Execution-time Merging of Multiple Clinical Workflows to Handle Comorbidity. PhD thesis, Dalhousie University.
  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., and Wishart, D. S. (2014). DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Research, 42(D1):1091- 1097. D1091-7, PubMed ID: 24203711.
  11. Lohr, K. N. (2003). Rating the strength of scientific evidence: relevance for quality improvement programs. International Journal for Quality in Health Care, 16(1):9-18.
  12. L ópez-Vallverd ú, J. A., Ria n˜o, D., and Collado, A. (2013). Rule-based combination of comorbid treatments for chronic diseases applied to hypertension, diabetes mellitus and heart failure. In LNCS, volume 7738 LNAI, pages 30-41.
  13. 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., and Schultes, E. (2011). The value of data. Nature Genetics, 43(4):281-283.
  14. Peleg, M. (2013). Computer-interpretable clinical guidelines: a methodological review. Journal of biomedical informatics, 46(4):744-63.
  15. Piovesan, L., Molino, G., and Terenziani, P. (2014). An ontological knowledge and multiple abstraction level decision support system in healthcare. Decision Analytics, 1(1):8.
  16. Wilk, S. and Michalowski, M. (2014). Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions. In Knowledge Representation for Health-Care (KR4HC). LNCS, vol. 8903, Berlin Heidelberg. Springer.
  17. Zamborlini, V., da Silveira, M., Pruski, C., ten Teije, A., and van Harmelen, F. (2014a). Towards a Conceptual Model for Enhancing Reasoning about Clinical Guidelines: A case-study on Comorbidity. In Knowledge Representation for Health-Care (KR4HC). LNCS, vol. 8903, Vienna, Austria. Springer Berlin Heidelberg.
  18. Zamborlini, V., da Silveira, M., Pruski, C., ten Teije, A., and van Harmelen, F. (2015a). Analyzing Recommendations Interactions in Clinical Guidelines: Impact of action type hierarchies and causation beliefs. In Artificial Inteligence in Medicine (AIME). LNCS,. Springer.
  19. Zamborlini, V., Hoekstra, R., da Silveira, M., Pruski, C., ten Teije, A., and van Harmelen, F. (2014b). A Conceptual Model for Detecting Interactions among Medical Recommendations in Clinical Guidelines. In Knowledge Engineering and Knowledge Management (EKAW). LNCS, vol. 8876, pages 591-606. Springer.
  20. Zamborlini, V., Hoekstra, R., da Silveira, M., Pruski, C., ten Teije, A., and van Harmelen, F. (2015b). Inferring Recommendation Interactions in Clinical Guidelines: Case-studies on Multimorbidity. Semantic Web Journal, Accepted, Open Acess.
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Paper Citation


in Harvard Style

Zamborlini V., Hoekstra R., Silveira M., Pruski C., Teije A. and Harmelen F. (2016). Generalizing the Detection of Internal and External Interactions in Clinical Guidelines . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 105-116. DOI: 10.5220/0005704101050116


in Bibtex Style

@conference{healthinf16,
author={Veruska Zamborlini and Rinke Hoekstra and Marcos da Silveira and Cedric Pruski and Annette ten Teije and Frank van Harmelen},
title={Generalizing the Detection of Internal and External Interactions in Clinical Guidelines},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)},
year={2016},
pages={105-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005704101050116},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)
TI - Generalizing the Detection of Internal and External Interactions in Clinical Guidelines
SN - 978-989-758-170-0
AU - Zamborlini V.
AU - Hoekstra R.
AU - Silveira M.
AU - Pruski C.
AU - Teije A.
AU - Harmelen F.
PY - 2016
SP - 105
EP - 116
DO - 10.5220/0005704101050116