J Biomed Inform - Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm.

Tópicos

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Resumo

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes' classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes' classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes' classifier using bigrams.

Resumo Limpo

paper describ applic call pefind documentlevel classif ct pulmonari angiographi report pefind base general version context algorithm simpl text process algorithm identifi featur clinic report document pefind use answer question diseas state pulmonari emboli present absent certainti state diagnosi uncertainti present absent tempor state identifi pulmonari embolus acut chronic technic qualiti state exam diagnost diagnost gold standard answer question determin consensus classif three human annot pefind result compar naiv bay classifi use unigram bigram sensit posit predict valu pefind diseas state qualiti state certainti state tempor state respect compar naiv bay classifi use unigram naiv bay classifi use bigram

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