AMIA Annu Symp Proc - Using UMLS lexical resources to disambiguate abbreviations in clinical text.

Tópicos

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Resumo

Clinical text is rich in acronyms and abbreviations, and they are highly ambiguous. As a pre-processing step before subsequent NLP analysis, we are developing and evaluating clinical abbreviation disambiguation methods. The evaluation of two sequential steps, the detection and the disambiguation of abbreviations, is reported here, for various types of clinical notes. For abbreviations detection, our result indicated the SPECIALIST Lexicon LRABR needed to be revised for better abbreviation detection. Our semi-supervised method using generated training data based on expanded form matching for 12 frequent abbreviations in our clinical notes reached over 90% accuracy in five-fold cross validation and unsupervised approach produced comparable results with the semi-supervised methods.

Resumo Limpo

clinic text rich acronym abbrevi high ambigu preprocess step subsequ nlp analysi develop evalu clinic abbrevi disambigu method evalu two sequenti step detect disambigu abbrevi report various type clinic note abbrevi detect result indic specialist lexicon lrabr need revis better abbrevi detect semisupervis method use generat train data base expand form match frequent abbrevi clinic note reach accuraci fivefold cross valid unsupervis approach produc compar result semisupervis method

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