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• W2294951813 abstract "We present our participation in Task 1a of the 2013 CLEF- eHEALTH Challenge, whose goal was the identication of disorder named entities from electronic medical records. We developed a supervised CRF model that based on a rich set of features learns to predict disorder named entities. The CRF system uses external knowledge from special- ized biomedical terminologies and Wikipedia. Our system performance was evaluated at 0.598 F-measure in the context of strict evaluation and 0.711 F-measure in the context of relaxed evaluation. of disorder named entities in electronic medical records, and develop a system that can perform NER on medical text. We pro- pose a solution that combines a rich feature set with external knowledge gathered from both specialized and general domain knowledge repositories. We present a named-entity recognition system specialized in the medical domain. Our system learns a CRF model from the training data, based on a rich feature set that combines external knowledge sources with information gathered from the EMR text. We discuss the system design, present the system results on the 2013 CLEF-eHEALTH (10) training and test data, and discuss the specic features that helped most with the system performance." @default.
• W2294951813 created "2016-06-24" @default.
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• W2294951813 date "2013-01-01" @default.
• W2294951813 modified "2023-10-04" @default.
• W2294951813 title "A Supervised Named-Entity Extraction System for Medical Text." @default.
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