J Am Med Inform Assoc - A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records.MATERIALS AND METHODS: A combination of supervised, unsupervised, and rule-based methods were used. Supervised methods include conditional random fields and support vector machines. A flexible automated feature selection technique was used to select the best subset of features for each supervised task. Unsupervised methods include Brown clustering on several corpora, which result in our method being considered semisupervised.RESULTS: On the 2012 Informatics for Integrating Biology and the Bedside (i2b2) shared task data, we achieved an overall event F1-measure of 0.8045, an overall temporal expression F1-measure of 0.6154, an overall temporal link detection F1-measure of 0.5594, and an end-to-end temporal link detection F1-measure of 0.5258. The most competitive system was our event recognition method, which ranked third out of the 14 participants in the event task.DISCUSSION: Analysis reveals the event recognition method has difficulty determining which modifiers to include/exclude in the event span. The temporal expression recognition method requires significantly more normalization rules, although many of these rules apply only to a small number of cases. Finally, the temporal relation recognition method requires more advanced medical knowledge and could be improved by separating the single discourse relation classifier into multiple, more targeted component classifiers.CONCLUSIONS: Recognizing events and temporal expressions can be achieved accurately by combining supervised and unsupervised methods, even when only minimal medical knowledge is available. Temporal normalization and temporal relation recognition, however, are far more dependent on the modeling of medical knowledge.

Resumo Limpo

jectiv provid natur languag process method automat recognit event tempor express tempor relat clinic recordsmateri method combin supervis unsupervis rulebas method use supervis method includ condit random field support vector machin flexibl autom featur select techniqu use select best subset featur supervis task unsupervis method includ brown cluster sever corpora result method consid semisupervisedresult informat integr biolog bedsid ib share task data achiev overal event fmeasur overal tempor express fmeasur overal tempor link detect fmeasur endtoend tempor link detect fmeasur competit system event recognit method rank third particip event taskdiscuss analysi reveal event recognit method difficulti determin modifi includeexclud event span tempor express recognit method requir signific normal rule although mani rule appli small number case final tempor relat recognit method requir advanc medic knowledg improv separ singl discours relat classifi multipl target compon classifiersconclus recogn event tempor express can achiev accur combin supervis unsupervis method even minim medic knowledg avail tempor normal tempor relat recognit howev far depend model medic knowledg

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