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Expert Opin Drug Saf. 2016 Sep;15(9):1153-61. doi: 10.1080/14740338.2016.1206075. Epub 2016 Jul 15.

MedDRA® automated term groupings using OntoADR: evaluation with upper gastrointestinal bleedings.

Author information

1
a INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142) , F-93430 , Villetaneuse , France.
2
b Department of Public Health and medical informatics , CHU University of Saint Etienne , Saint-Etienne , France.
3
c Gastroenterology Department , CHU of Saint-Etienne , Saint-Etienne , France.
4
d Sorbonne universités, Université de technologie de Compiègne, EA 2223 Costech (Connaissance, Organisation et Systèmes Techniques), Centre Pierre Guillaumat , Compiègne CEDEX , France.

Abstract

OBJECTIVE:

To propose a method to build customized sets of MedDRA terms for the description of a medical condition. We illustrate this method with upper gastrointestinal bleedings (UGIB).

RESEARCH DESIGN AND METHODS:

We created a broad list of MedDRA terms related to UGIB and defined a gold standard with the help of experts. MedDRA terms were formally described in a semantic resource named OntoADR. We report the use of two semantic queries that automatically select candidate terms for UGIB. Query 1 is a combination of two SNOMED CT concepts describing both morphology 'Hemorrhage' and finding site 'Upper digestive tract structure'. Query 2 complements Query 1 by taking into account MedDRA terms associated to SNOMED CT concepts describing clinical manifestations 'Melena' or 'Hematemesis'.

RESULTS:

We compared terms in queries and our gold standard achieving a recall of 71.0% and a precision of 81.4% for query 1 (F1 score 0.76); and a recall of 96.7% and a precision of 77.0% for query 2 (F1 score 0.86).

CONCLUSIONS:

Our results demonstrate the feasibility of applying knowledge engineering techniques for building customized sets of MedDRA terms. Additional work is necessary to improve precision and recall, and confirm the interest of the proposed strategy.

KEYWORDS:

MedDRA; OntoADR; Pharmacovigilance; SNOMED CT; UGIB; ontology; signal detection

PMID:
27348725
DOI:
10.1080/14740338.2016.1206075
[Indexed for MEDLINE]

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