Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD

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

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review


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.

Original languageEnglish
Title of host publicationBiomedical Engineering Systems and Technologies: 9th International Joint Conference, BIOSTEC 2016, Rome, Italy, February 21--23, 2016, Revised Selected Papers
EditorsAna Fred, Hugo Gamboa
Place of PublicationCham
PublisherSpringer International Publishing Switzerland
Number of pages27
ISBN (Print)978-3-319-54717-6
Publication statusPublished - 2017


  • Clinical guidelines
  • Knowledge representation
  • Ontologies
  • Semantic web


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