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. Method: 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. Discussion: A knowledge based approach improved the performance of signal detection tools. However, the huge workload involved in the knowledge engineering step limits the use of this approach for machine learning.