J Am Med Inform Assoc - Pneumonia identification using statistical feature selection.

Tópicos

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Resumo

JECTIVE: This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia.DESIGN: A binary classifier was employed to identify pneumonia from a dataset of multiple types of clinical notes created for 426 patients during their stay in the intensive care unit. For this purpose, three types of features were considered: (1) word n-grams, (2) Unified Medical Language System (UMLS) concepts, and (3) assertion values associated with pneumonia expressions. System performance was greatly increased by a feature selection approach which uses statistical significance testing to rank features based on their association with the two categories of pneumonia identification.RESULTS: Besides testing our system on the entire cohort of 426 patients (unrestricted dataset), we also used a smaller subset of 236 patients (restricted dataset). The performance of the system was compared with the results of a baseline previously proposed for these two datasets. The best results achieved by the system (85.71 and 81.67 F1-measure) are significantly better than the baseline results (50.70 and 49.10 F1-measure) on the restricted and unrestricted datasets, respectively.CONCLUSION: Using a statistical feature selection approach that allows the feature extractor to consider only the most informative features from the feature space significantly improves the performance over a baseline that uses all the features from the same feature space. Extracting the assertion value for pneumonia expressions further improves the system performance.

Resumo Limpo

jectiv paper describ natur languag process system task pneumonia identif base inform extract narrat report associ patient task identifi whether patient posit pneumoniadesign binari classifi employ identifi pneumonia dataset multipl type clinic note creat patient stay intens care unit purpos three type featur consid word ngram unifi medic languag system uml concept assert valu associ pneumonia express system perform great increas featur select approach use statist signific test rank featur base associ two categori pneumonia identificationresult besid test system entir cohort patient unrestrict dataset also use smaller subset patient restrict dataset perform system compar result baselin previous propos two dataset best result achiev system fmeasur signific better baselin result fmeasur restrict unrestrict dataset respectivelyconclus use statist featur select approach allow featur extractor consid inform featur featur space signific improv perform baselin use featur featur space extract assert valu pneumonia express improv system perform

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