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| Content Provider | ACM Digital Library |
|---|---|
| Author | Grabar, Natalia Bousquet, Cédric Dupuch, Marie |
| Abstract | Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. The detection of adverse drug reactions is performed thanks to statistical algorithms and to groupings of ADR terms. Standardized MedDRA Queries (SMQs) are the groupings which become a standard for assisting the retrieval and evaluation of MedDRA-coded ADR reports all through the world. Currently 84 SMQs have been created manually by experts, while several important safety topics are not yet covered. Dependent on the context of their application, these SMQs show a high degree of sensitivity and often appear to be over-inclusive. For pharmacovigilance experts it represents an important and tedious filtering of data. The objective of this work is to propose an automatic method for assisting the creation of SMQs and also for the refinement of their organization further to the creation of smaller clusters of ADR terms. In this work we propose to exploit the semantic distance and clustering approaches. We perform several experiments and vary several parameters of the method. |
| Starting Page | 181 |
| Ending Page | 190 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450307819 |
| DOI | 10.1145/2110363.2110386 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-01-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Meddra Pharmacovigilance Semantic distance Clustering of terms |
| Content Type | Text |
| Resource Type | Article |
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