Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Conceptual Graph Matching for Semantic Search
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Improving the mapping between MedDRA and SNOMED CT
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
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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.