J Biomed Inform - Towards generating a patient's timeline: extracting temporal relationships from clinical notes.

Tópicos

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Resumo

Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.

Resumo Limpo

clinic record includ code freetext field interact reflect complic patient stori inform often cover present medic condit event experienc patient also refer relev event past sign symptom test treatment order automat construct timelin event first need extract tempor relat pair event time express present clinic note design separ extract compon differ type tempor relat util novel hybrid system combin machin learn graphbas infer mechan extract tempor link tempor graph direct graph base pars tree depend simplifi sentenc frequent pattern clue general sentenc order discov pattern given complex natur languag might direct discover origin sentenc propos hybrid system perform reach fmeasur precis recal ib natur languag process corpus tempor relat tlink extract task achiev highest precis third highest fmeasur among particip team tlink track

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