A knowledge based approach for automated signal generation in pharmacovigilance.
BACKGROUND
Pharmacovigilance experts detect new adverse drug reactions (ADR) by manually reviewing spontaneous reporting systems. Automated signal generation aims to focus the attention of experts on drug-adverse event associations which are disproportionally present in the database. Although adverse events are coded by means of controlled vocabularies such as the MedDRA dictionary, this semantic information is not taken into account for signal generation.


OBJECTIVE
To improve the performance of current signal detection algorithms using knowledge based approach.


METHODS
We developed a formal ontology of ADRs and built a data mining tool that uses description logic representations of MedDRA terms to group medically related case reports.


RESULTS
This knowledge based approach increased the sensitivity of signal detection with no decrease in specificity.


CONCLUSIONS
A knowledge based approach improved the performance of signal detection tools. However, the huge work-load involved in the knowledge engineering step limits the use of this approach for machine learning.
Henegar
Corneliu
C
Laboratoire de Santé Publique et d'Informatique Médicale, INSERM ERM 202, Faculté de médecine Broussais Hôtel Dieu, 15 rue de l'Ecole de Médecine, 75006 Paris, France. corneliu.henegar@spim.jussieu.fr
Bousquet
Cédric
C
Lillo-Le Louët
Agnès
A
Degoulet
Patrice
P
Jaulent
Marie-Christine
MC
D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D001330:Electronic Data Processing; D005602:France; D006801:Humans; D009626:Terminology as Topic; D018875:Vocabulary, Controlled