J Am Med Inform Assoc - Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification.

Tópicos

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Resumo

CKGROUND: Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.OBJECTIVE: To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text.MATERIALS AND METHODS: The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework.RESULTS: Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537.CONCLUSIONS: Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance.

Resumo Limpo

ckground tempor inform detect system develop mayo clinic ib natur languag process challengeobject construct autom system eventtimex extract tempor link tlink identif clinic textmateri method ib organ provid annot discharg summari train set discharg summari test set event system use condit random field classifi varieti featur includ lexic inform natur languag element medic ontolog timex system employ rulebas method use regular express pattern match systemat reason determin normal valu tlink system employ rulebas reason machin learn three system built apach unstructur inform manag architectur frameworkresult timex system perform best fmeasur valu accuraci among challeng team event system produc fmeasur tlink system fmeasur conclus timex system demonstr good capabl regular express rule extract normal time inform event tlink machin learn system requir welldefin featur set perform well also leverag expert knowledg part machin learn featur improv tlink identif perform

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