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Agregar informaciónUsing machine learning for concept extraction on clinical documents from multiple data sources.
ISSN
J Am Med Inform Assoc.
18
5
580-7
27 de Junio de 2011
Ingles
doi: 10.1136/amiajnl-2011-000155
amiajnl-2011-000155.pdf
Concept extraction is a process to identify phrases referring to concepts of interests in unstructured text. In clinical text processing,1 2 specific concept extraction tasks include, for example, detection of phrases referring to disorders, for example, ‘. prior history of tonsil cancerDisorder ’ (an example taken from the paper by Ogren et al3). Concept extraction is a subtask of information extraction (IE) that facilitates automated acquisition of structured information from text, and it has been studied across multiple domains, including news articles and biological research literature.4e6 Over the last decade, machine learning methods have achieved excellent performance in concept extraction.
Abstract
OBJECTIVE: Concept extraction is a process to identify phrases referring to concepts of interests in unstructured text. It is a critical component in automated text processing. We investigate the performance of machine learning taggers for clinical concept extraction, particularly the portability of taggers across documents from multiple data sources.
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Manabu Torii,1,2 Kavishwar Wagholikar,1,3 Hongfang Liu