  Pharmacovigilance is the field of science devoted to the collection, analysis
and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the
extraction of information about ADRs from free text resources are essential to
support the work of experts, employed in the crucial task of detecting and
classifying unexpected pathologies possibly related to drug assumptions.
Narrative ADR descriptions may be collected in several way, e.g. by monitoring
social networks or through the so called spontaneous reporting, the main method
pharmacovigilance adopts in order to identify ADRs. The encoding of free-text
ADR descriptions according to MedDRA standard terminology is central for report
analysis. It is a complex work, which has to be manually implemented by the
pharmacovigilance experts. The manual encoding is expensive (in terms of time).
Moreover, a problem about the accuracy of the encoding may occur, since the
number of reports is growing up day by day. In this paper, we propose
MagiCoder, an efficient Natural Language Processing algorithm able to
automatically derive MedDRA terminologies from free-text ADR descriptions.
MagiCoder is part of VigiWork, a web application for online ADR reporting and
analysis. From a practical view-point, MagiCoder radically reduces the revision
time of ADR reports: the pharmacologist has simply to revise and validate the
automatic solution versus the hard task of choosing solutions in the 70k terms
of MedDRA. This improvement of the expert work efficiency has a meaningful
impact on the quality of data analysis. Moreover, our procedure is general
purpose. We developed MagiCoder for the Italian pharmacovigilance language, but
preliminarily analyses show that it is robust to language and dictionary
changes.
