PEGASE: a knowledge graph for search and exploration in pharmacovigilance data
EKAW Posters and Demonstrations, 2018•inria.hal.science
Pharmacovigilance is in charge of studying the adverse effects of pharmaceutical products.
In this field, pharmacovigilance specialists experience several difficulties when searching
and exploring their patient data despite the existence of standardized terminologies
(MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance
specialists perform search and exploration on their data. First, we have developed a
knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA …
In this field, pharmacovigilance specialists experience several difficulties when searching
and exploring their patient data despite the existence of standardized terminologies
(MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance
specialists perform search and exploration on their data. First, we have developed a
knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA …
Pharmacovigilance is in charge of studying the adverse effects of pharmaceutical products. In this field, pharmacovigilance specialists experience several difficulties when searching and exploring their patient data despite the existence of standardized terminologies (MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance specialists perform search and exploration on their data. First, we have developed a knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA terminology with SNOMED CT concepts, and that includes anonymized patient data from FAERS. Second, we have chosen and applied a semantic search tool, Sparklis, according to the user requirements that we have identified in pharmacovigilance.
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