TY - GEN
T1 - Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD
AU - Carretta Zamborlini, Veruska
AU - Hoekstra, Rinke
AU - Da Silveira, Marcos
AU - Pruski, Cedric
AU - ten Teije, Annette
AU - van Harmelen, Frank
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Clinical guidelines
KW - Knowledge representation
KW - Ontologies
KW - Semantic web
UR - http://www.scopus.com/inward/record.url?scp=85014898888&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-54717-6_20
DO - 10.1007/978-3-319-54717-6_20
M3 - Conference contribution
SN - 9783319547169
T3 - Communications in Computer and Information Science
SP - 360
EP - 386
BT - Biomedical Engineering Systems and Technologies
A2 - Gamboa, Hugo
A2 - Fred, Ana
PB - Springer International Publishing Switzerland
CY - Cham
ER -