J Am Med Inform Assoc - A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction.

Tópicos

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Resumo

JECTIVE: The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora.DATA AND METHODS: We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes. We evaluated the system's performance against the human-generated gold standard.RESULTS: The experiments showed that the two machine learning approaches performed statistically significantly better than the baseline rule-based approach. The binary SVM classification achieved 0.94 F-measure with individual tokens as features. The SVM model trained on a parsimonious feature set achieved 0.81 F-measure for CN and 0.87 for CTA. The CRF MLSL method achieved 0.80 F-measure on both corpora.DISCUSSION AND CONCLUSIONS: We compared the novel MLSL method with a binary classification and a rule-based method. The MLSL method performed statistically significantly better than the rule-based method. However, the SVM-based binary classification method was statistically significantly better than the MLSL method for both the CTA and CN corpora. Using parsimonious feature sets both the SVM-based binary classification and CRF-based MLSL methods achieved high performance in detecting medication name and attribute linkages in CTA and CN.

Resumo Limpo

jectiv goal work evalu machin learn method binari classif sequenc label medicationattribut linkag detect two clinic corporadata method doubl annot clinic trial announc cta clinic note cn medic name entiti attribut binari support vector machin svm classif method parsimoni featur set condit random field crfbase multilay sequenc label mlsl model propos identifi linkag entiti correspond attribut evalu system perform humangener gold standardresult experi show two machin learn approach perform statist signific better baselin rulebas approach binari svm classif achiev fmeasur individu token featur svm model train parsimoni featur set achiev fmeasur cn cta crf mlsl method achiev fmeasur corporadiscuss conclus compar novel mlsl method binari classif rulebas method mlsl method perform statist signific better rulebas method howev svmbase binari classif method statist signific better mlsl method cta cn corpora use parsimoni featur set svmbase binari classif crfbase mlsl method achiev high perform detect medic name attribut linkag cta cn

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