AMIA Annu Symp Proc - Combining corpus-derived sense profiles with estimated frequency information to disambiguate clinical abbreviations.

Tópicos

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Resumo

Abbreviations are widely used in clinical notes and are often ambiguous. Word sense disambiguation (WSD) for clinical abbreviations therefore is a critical task for many clinical natural language processing (NLP) systems. Supervised machine learning based WSD methods are known for their high performance. However, it is time consuming and costly to construct annotated samples for supervised WSD approaches and sense frequency information is often ignored by these methods. In this study, we proposed a profile-based method that used dictated discharge summaries as an external source to automatically build sense profiles and applied them to disambiguate abbreviations in hospital admission notes via the vector space model. Our evaluation using a test set containing 2,386 annotated instances from 13 ambiguous abbreviations in admission notes showed that the profile-based method performed better than two baseline methods and achieved a best average precision of 0.792. Furthermore, we developed a strategy to combine sense frequency information estimated from a clustering analysis with the profile-based method. Our results showed that the combined approach largely improved the performance and achieved a highest precision of 0.875 on the same test set, indicating that integrating sense frequency information with local context is effective for clinical abbreviation disambiguation.

Resumo Limpo

abbrevi wide use clinic note often ambigu word sens disambigu wsd clinic abbrevi therefor critic task mani clinic natur languag process nlp system supervis machin learn base wsd method known high perform howev time consum cost construct annot sampl supervis wsd approach sens frequenc inform often ignor method studi propos profilebas method use dictat discharg summari extern sourc automat build sens profil appli disambigu abbrevi hospit admiss note via vector space model evalu use test set contain annot instanc ambigu abbrevi admiss note show profilebas method perform better two baselin method achiev best averag precis furthermor develop strategi combin sens frequenc inform estim cluster analysi profilebas method result show combin approach larg improv perform achiev highest precis test set indic integr sens frequenc inform local context effect clinic abbrevi disambigu

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