J Biomed Inform - Automatically extracting information needs from complex clinical questions.

Tópicos

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Resumo

JECTIVE: Clinicians pose complex clinical questions when seeing patients, and identifying the answers to those questions in a timely manner helps improve the quality of patient care. We report here on two natural language processing models, namely, automatic topic assignment and keyword identification, that together automatically and effectively extract information needs from ad hoc clinical questions. Our study is motivated in the context of developing the larger clinical question answering system AskHERMES (Help clinicians to Extract and aRrticulate Multimedia information for answering clinical quEstionS).DESIGN AND MEASUREMENTS: We developed supervised machine-learning systems to automatically assign predefined general categories (e.g. etiology, procedure, and diagnosis) to a question. We also explored both supervised and unsupervised systems to automatically identify keywords that capture the main content of the question.RESULTS: We evaluated our systems on 4654 annotated clinical questions that were collected in practice. We achieved an F1 score of 76.0% for the task of general topic classification and 58.0% for keyword extraction. Our systems have been implemented into the larger question answering system AskHERMES. Our error analyses suggested that inconsistent annotation in our training data have hurt both question analysis tasks.CONCLUSION: Our systems, available at http://www.askhermes.org, can automatically extract information needs from both short (the number of word tokens <20) and long questions (the number of word tokens >20), and from both well-structured and ill-formed questions. We speculate that the performance of general topic classification and keyword extraction can be further improved if consistently annotated data are made available.

Resumo Limpo

jectiv clinician pose complex clinic question see patient identifi answer question time manner help improv qualiti patient care report two natur languag process model name automat topic assign keyword identif togeth automat effect extract inform need ad hoc clinic question studi motiv context develop larger clinic question answer system askherm help clinician extract arrticul multimedia inform answer clinic questionsdesign measur develop supervis machinelearn system automat assign predefin general categori eg etiolog procedur diagnosi question also explor supervis unsupervis system automat identifi keyword captur main content questionresult evalu system annot clinic question collect practic achiev f score task general topic classif keyword extract system implement larger question answer system askherm error analys suggest inconsist annot train data hurt question analysi tasksconclus system avail httpwwwaskhermesorg can automat extract inform need short number word token long question number word token wellstructur illform question specul perform general topic classif keyword extract can improv consist annot data made avail

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