AMIA Annu Symp Proc - Towards a semantic lexicon for clinical natural language processing.

Tópicos

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{ data(3963) clinic(1234) research(1004) }
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{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

A semantic lexicon which associates words and phrases in text to concepts is critical for extracting and encoding clinical information in free text and therefore achieving semantic interoperability between structured and unstructured data in Electronic Health Records (EHRs). Directly using existing standard terminologies may have limited coverage with respect to concepts and their corresponding mentions in text. In this paper, we analyze how tokens and phrases in a large corpus distribute and how well the UMLS captures the semantics. A corpus-driven semantic lexicon, MedLex, has been constructed where the semantics is based on the UMLS assisted with variants mined and usage information gathered from clinical text. The detailed corpus analysis of tokens, chunks, and concept mentions shows the UMLS is an invaluable source for natural language processing. Increasing the semantic coverage of tokens provides a good foundation in capturing clinical information comprehensively. The study also yields some insights in developing practical NLP systems.

Resumo Limpo

semant lexicon associ word phrase text concept critic extract encod clinic inform free text therefor achiev semant interoper structur unstructur data electron health record ehr direct use exist standard terminolog may limit coverag respect concept correspond mention text paper analyz token phrase larg corpus distribut well uml captur semant corpusdriven semant lexicon medlex construct semant base uml assist variant mine usag inform gather clinic text detail corpus analysi token chunk concept mention show uml invalu sourc natur languag process increas semant coverag token provid good foundat captur clinic inform comprehens studi also yield insight develop practic nlp system

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