AMIA Annu Symp Proc - Assessing pneumonia identification from time-ordered narrative reports.

Tópicos

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{ survey(1388) particip(1329) question(1065) }
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{ method(2212) result(1239) propos(1039) }

Resumo

In this paper, we present a natural language processing system that can be used in hospital surveillance applications with the purpose of identifying patients with pneumonia. For this purpose, we built a sequence of supervised classifiers, where the dataset corresponding to each classifier consists of a restricted set of time-ordered narrative reports. In this way the pneumonia surveillance application will be able to invoke the most suitable classifier for each patient based on the period of time that has elapsed since the patient was admitted into the hospital. Our system achieves significantly better results when compared with a baseline previously proposed for pneumonia identification.

Resumo Limpo

paper present natur languag process system can use hospit surveil applic purpos identifi patient pneumonia purpos built sequenc supervis classifi dataset correspond classifi consist restrict set timeord narrat report way pneumonia surveil applic will abl invok suitabl classifi patient base period time elaps sinc patient admit hospit system achiev signific better result compar baselin previous propos pneumonia identif

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