J Biomed Inform - MedTime: a temporal information extraction system for clinical narratives.

Tópicos

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Resumo

Temporal information extraction from clinical narratives is of critical importance to many clinical applications. We participated in the EVENT/TIMEX3 track of the 2012 i2b2 clinical temporal relations challenge, and presented our temporal information extraction system, MedTime. MedTime comprises a cascade of rule-based and machine-learning pattern recognition procedures. It achieved a micro-averaged f-measure of 0.88 in both the recognitions of clinical events and temporal expressions. We proposed and evaluated three time normalization strategies to normalize relative time expressions in clinical texts. The accuracy was 0.68 in normalizing temporal expressions of dates, times, durations, and frequencies. This study demonstrates and evaluates the integration of rule-based and machine-learning-based approaches for high performance temporal information extraction from clinical narratives.

Resumo Limpo

tempor inform extract clinic narrat critic import mani clinic applic particip eventtimex track ib clinic tempor relat challeng present tempor inform extract system medtim medtim compris cascad rulebas machinelearn pattern recognit procedur achiev microaverag fmeasur recognit clinic event tempor express propos evalu three time normal strategi normal relat time express clinic text accuraci normal tempor express date time durat frequenc studi demonstr evalu integr rulebas machinelearningbas approach high perform tempor inform extract clinic narrat

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