Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Topic Signature Language Models for Ad hoc Retrieval
IEEE Transactions on Knowledge and Data Engineering
The AMTEx approach in the medical document indexing and retrieval application
Data & Knowledge Engineering
Multiple Terminologies in a Health Portal: Automatic Indexing and Information Retrieval
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
MaxMatcher: biological concept extraction using approximate dictionary lookup
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Combining global and local semantic contexts for improving biomedical information retrieval
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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We are interested in retrieving relevant information from biomedical documents according to healthcare professional's information needs. It is well known that biomedical documents are indexed using conceptual descriptors issued from terminologies for a better retrieval performance. Our attempt to develop a conceptual retrieval framework relies on the hypothesis that there are several broad categories of knowledge that could be captured from different terminologies and processed by retrieval algorithms. With this in mind, we propose a multiterminology based indexing approach for selecting the best representative concepts for each document. We instantiate this general approach on four terminologies namely MeSH (Medical Subject Headings), SNOMED (Systematized Nomenclature of Medicine), ICD-10 (International Classification of Diseases) and GO (Gene Ontology). Experimental studies were conducted on large and official document test collections of real world clinical queries and associated judgments extracted from MEDLINE scientific collections, namely TREC Genomics 2004 & 2005. The obtained results demonstrate the advantages of our multi-terminology based biomedical information retrieval approach over state-of-the art approaches.