J Biomed Inform - A new clustering method for detecting rare senses of abbreviations in clinical notes.

Tópicos

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Resumo

Abbreviations are widely used in clinical documents and they are often ambiguous. Building a list of possible senses (also called sense inventory) for each ambiguous abbreviation is the first step to automatically identify correct meanings of abbreviations in given contexts. Clustering based methods have been used to detect senses of abbreviations from a clinical corpus [1]. However, rare senses remain challenging and existing algorithms are not good enough to detect them. In this study, we developed a new two-phase clustering algorithm called Tight Clustering for Rare Senses (TCRS) and applied it to sense generation of abbreviations in clinical text. Using manually annotated sense inventories from a set of 13 ambiguous clinical abbreviations, we evaluated and compared TCRS with the existing Expectation Maximization (EM) clustering algorithm for sense generation, at two different levels of annotation cost (10 vs. 20 instances for each abbreviation). Our results showed that the TCRS-based method could detect 85% senses on average; while the EM-based method found only 75% senses, when similar annotation effort (about 20 instances) was used. Further analysis demonstrated that the improvement by the TCRS method was mainly from additionally detected rare senses, thus indicating its usefulness for building more complete sense inventories of clinical abbreviations.

Resumo Limpo

abbrevi wide use clinic document often ambigu build list possibl sens also call sens inventori ambigu abbrevi first step automat identifi correct mean abbrevi given context cluster base method use detect sens abbrevi clinic corpus howev rare sens remain challeng exist algorithm good enough detect studi develop new twophas cluster algorithm call tight cluster rare sens tcrs appli sens generat abbrevi clinic text use manual annot sens inventori set ambigu clinic abbrevi evalu compar tcrs exist expect maxim em cluster algorithm sens generat two differ level annot cost vs instanc abbrevi result show tcrsbase method detect sens averag embas method found sens similar annot effort instanc use analysi demonstr improv tcrs method main addit detect rare sens thus indic use build complet sens inventori clinic abbrevi

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