J Am Med Inform Assoc - A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

Tópicos

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Resumo

JECTIVE: The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge.DESIGN: The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes.MEASUREMENTS: Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge.RESULTS AND DISCUSSION: Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test data set in the challenge.

Resumo Limpo

jectiv author goal develop evalu machinelearningbas approach extract clinic entitiesinclud medic problem test treatment well assert statusfrom hospit discharg summari written use natur languag project part center informat integr biolog bedsideveteran affair va naturallanguageprocess challengedesign author implement machinelearningbas name entiti recognit system clinic text systemat evalu contribut differ type featur ml algorithm use train corpus annot note base result train data author develop novel hybrid clinic entiti extract system integr heurist rulebas modul mlbase name entiti recognit modul author appli hybrid system concept extract assert classif task challeng evalu perform use test data set annot notesmeasur standard measur includ precis recal fmeasur calcul use evalu script provid center informat integr biolog bedsideva challeng organ overal perform three type clinic entiti six type assert across annot note consid primari metric challengeresult discuss systemat evalu train set show condit random field outperform support vector machin semant inform exist naturallanguageprocess system larg improv perform although contribut differ type featur vari author hybrid entiti extract system achiev maximum overal fscore concept extract rank second assert classif rank fourth statist differ first three system test data set challeng

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