J Biomed Inform - Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Tópicos

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Resumo

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific na?ve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system. It was also assessed on the manually annotated, 2010 i2b2 challenge data. Performance was established with regard to precision, recall, and F-measure for each of the concepts within the VA documents using bootstrapping. Within that corpus, concepts identified by MCVS were broadly distributed throughout SNOMED CT, and the token-order-specific language model achieved better performance based on precision, recall, and F-measure (0.95?0.15, 0.96?0.16, and 0.95?0.16, respectively; mean?SD) than the bag-of-words based, na?ve Bayes model (0.64?0.45, 0.61?0.46, and 0.60?0.45, respectively) that has previously been used for concept mapping. Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model. RapTAT required just 7.2ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific na?ve Bayes model and machine learning.

Resumo Limpo

rapid autom determin map free text phrase predefin concept assist annot clinic note increas speed natur languag process system aim studi design evalu tokenorderspecif nave bayesbas machin learn system raptat predict associ phrase concept perform assess use refer standard generat va discharg summari contain phrase map distinct systemat nomenclatur medicin clinic term snome ct concept mcvs natur languag process system also assess manual annot ib challeng data perform establish regard precis recal fmeasur concept within va document use bootstrap within corpus concept identifi mcvs broad distribut throughout snome ct tokenorderspecif languag model achiev better perform base precis recal fmeasur respect meansd bagofword base nave bay model respect previous use concept map precis recal fmeasur ib test set respect use tokenorderspecif model raptat requir just ms map phrase within singl discharg summari map rate decreas number process document increas high perform attain tool term accuraci speed encourag map rate suffici support nearrealtim interact annot medic narrat result demonstr feasibl rapid accur map phrase wide rang medic concept base tokenorderspecif nave bay model machin learn

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