Corpus ID: 209444762

MedCAT - Medical Concept Annotation Tool

@article{Kraljevi2019MedCATM,
  title={MedCAT - Medical Concept Annotation Tool},
  author={Željko Kraljevi{\'c} and Daniel Bean and Aurelie Mascio and Lukasz Roguski and Amos Folarin and Angus Roberts and Rebecca Bendayan and Richard J. B. Dobson},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.10166}
}
  • Željko Kraljević, Daniel Bean, +5 authors Richard J. B. Dobson
  • Published in ArXiv 2019
  • Computer Science, Mathematics
  • Biomedical documents such as Electronic Health Records (EHRs) contain a large amount of information in an unstructured format. The data in EHRs is a hugely valuable resource documenting clinical narratives and decisions, but whilst the text can be easily understood by human doctors it is challenging to use in research and clinical applications. To uncover the potential of biomedical documents we need to extract and structure the information they contain. The task at hand is Named Entity… CONTINUE READING

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