[A review of potential signals generated by an automated method on 3324 pharmacovigilance case reports]. / Revue des signaux générés par une méthode automatisée sur 3324 cas de pharmacovigilance.

Automated signal generation aims to focus the attention of pharmacovigilance experts on drug-ADR associations which are disproportionally present in a spontaneous reporting system. Since 1986, we could find several signals using classic pharmacovigilance techniques with case reports registered in our pharmacovigilance regional centre. From this dataset 3,324 cases were related to spontaneous reporting. Drug-ADR associations were generated by using a Data Mining Algorithm (DMA) proposed by Evans et al. Potential signals were evaluated by reviewing case reports related to the unlabelled associations. The DMA generated 523 associations of which 107 were not described in the SPC. Most potential signals were false positives. Although the DMA generated little additional knowledge compared to signals already detected using classic techniques, the whole process helped us to focus our case review on a very small subset of the whole dataset (9.6%).