J Am Med Inform Assoc - Evaluating temporal relations in clinical text: 2012 i2b2 Challenge.

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Resumo

CKGROUND: The Sixth Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing Challenge for Clinical Records focused on the temporal relations in clinical narratives. The organizers provided the research community with a corpus of discharge summaries annotated with temporal information, to be used for the development and evaluation of temporal reasoning systems. 18 teams from around the world participated in the challenge. During the workshop, participating teams presented comprehensive reviews and analysis of their systems, and outlined future research directions suggested by the challenge contributions.METHODS: The challenge evaluated systems on the information extraction tasks that targeted: (1) clinically significant events, including both clinical concepts such as problems, tests, treatments, and clinical departments, and events relevant to the patient's clinical timeline, such as admissions, transfers between departments, etc; (2) temporal expressions, referring to the dates, times, durations, or frequencies phrases in the clinical text. The values of the extracted temporal expressions had to be normalized to an ISO specification standard; and (3) temporal relations, between the clinical events and temporal expressions. Participants determined pairs of events and temporal expressions that exhibited a temporal relation, and identified the temporal relation between them.RESULTS: For event detection, statistical machine learning (ML) methods consistently showed superior performance. While ML and rule based methods seemed to detect temporal expressions equally well, the best systems overwhelmingly adopted a rule based approach for value normalization. For temporal relation classification, the systems using hybrid approaches that combined ML and heuristics based methods produced the best results.

Resumo Limpo

ckground sixth informat integr biolog bedsid ib natur languag process challeng clinic record focus tempor relat clinic narrat organ provid research communiti corpus discharg summari annot tempor inform use develop evalu tempor reason system team around world particip challeng workshop particip team present comprehens review analysi system outlin futur research direct suggest challeng contributionsmethod challeng evalu system inform extract task target clinic signific event includ clinic concept problem test treatment clinic depart event relev patient clinic timelin admiss transfer depart etc tempor express refer date time durat frequenc phrase clinic text valu extract tempor express normal iso specif standard tempor relat clinic event tempor express particip determin pair event tempor express exhibit tempor relat identifi tempor relat themresult event detect statist machin learn ml method consist show superior perform ml rule base method seem detect tempor express equal well best system overwhelm adopt rule base approach valu normal tempor relat classif system use hybrid approach combin ml heurist base method produc best result

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