| Literature DB >> 17238741 |
Herman Tolentino1, Michael Matters, Wikke Walop, Barbara Law, Wesley Tong, Fang Liu, Paul Fontelo, Katrin Kohl, Daniel Payne.
Abstract
Large amounts of information are locked up in free text components of clinical reports. Surveillance systems that monitor adverse events following immunizations (AEFI) can utilize these components after concept extraction using natural language processing (NLP). Specifically, our method for the identification and filtering of negated concepts using the Unified Medical Language System (UMLS) potentially improves the quality of AEFI surveillance systems.Mesh：
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Year: 2006 PMID： 17238741 PMCID： PMC1839642
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076