NCBI at 2013 ShARe/CLEF eHealth Shared Task: Disorder Normalization in Clinical Notes with Dnorm

@inproceedings{Leaman2013NCBIA2,
  title={NCBI at 2013 ShARe/CLEF eHealth Shared Task: Disorder Normalization in Clinical Notes with Dnorm},
  author={Robert Leaman and Ritu Khare and Zhiyong Lu},
  booktitle={CLEF},
  year={2013}
}
We describe an application of DNorm – a mathematically principled and high performing methodology for disease recognition and normalization, even in the presence of term variation – to clinical notes. DNorm consists of a text processing pipeline, including the BANNER named entity recognizer to locate diseases in the text, and a novel machine learning approach based on pairwise learning to rank to normalize the recognized mentions to concepts within a controlled lexicon. DNorm achieved the… CONTINUE READING
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Three Shared Tasks on Clinical Natural Language Processing

  • H. Suominen, S. Salantera, S Velupillai
  • Proceedings of the Conference and Labs of the…
  • 2013
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Enabling Recognition of Diseases in Biomedical Text with Machine Learning: Corpus and Benchmark

  • R. Leaman, C. Miller, G. Gonzalez
  • Proceedings of the 2009 Symposium on Languages in…
  • 2009
2 Excerpts

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