J Biomed Inform - A text processing pipeline to extract recommendations from radiology reports.

Tópicos

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Resumo

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology recommendations is an important barrier to ensuring timely follow-up of patients especially with non-acute incidental findings on imaging examinations. In this paper, we present a text processing pipeline to automatically identify clinically important recommendation sentences in radiology reports. Our extraction pipeline is based on natural language processing (NLP) and supervised text classification methods. To develop and test the pipeline, we created a corpus of 800 radiology reports double annotated for recommendation sentences by a radiologist and an internist. We ran several experiments to measure the impact of different feature types and the data imbalance between positive and negative recommendation sentences. Our fully statistical approach achieved the best f-score 0.758 in identifying the critical recommendation sentences in radiology reports.

Resumo Limpo

communic followup recommend abnorm identifi imag studi prone error absenc autom system identifi track radiolog recommend import barrier ensur time followup patient especi nonacut incident find imag examin paper present text process pipelin automat identifi clinic import recommend sentenc radiolog report extract pipelin base natur languag process nlp supervis text classif method develop test pipelin creat corpus radiolog report doubl annot recommend sentenc radiologist internist ran sever experi measur impact differ featur type data imbal posit negat recommend sentenc fulli statist approach achiev best fscore identifi critic recommend sentenc radiolog report

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