Automated recognition of malignancy mentions in biomedical literature |
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Authors: | Yang Jin Ryan T McDonald Kevin Lerman Mark A Mandel Steven Carroll Mark Y Liberman Fernando C Pereira Raymond S Winters and Peter S White |
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Institution: | (1) Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA;(2) Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA;(3) The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;(4) Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA 19104, USA |
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Abstract: | Background The rapid proliferation of biomedical text makes it increasingly difficult for researchers to identify, synthesize, and utilize
developed knowledge in their fields of interest. Automated information extraction procedures can assist in the acquisition
and management of this knowledge. Previous efforts in biomedical text mining have focused primarily upon named entity recognition
of well-defined molecular objects such as genes, but less work has been performed to identify disease-related objects and
concepts. Furthermore, promise has been tempered by an inability to efficiently scale approaches in ways that minimize manual
efforts and still perform with high accuracy. Here, we have applied a machine-learning approach previously successful for
identifying molecular entities to a disease concept to determine if the underlying probabilistic model effectively generalizes
to unrelated concepts with minimal manual intervention for model retraining. |
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