@InProceedings{weegar-nygrard-dalianis:2017:RANLP,
  author    = {Weegar, Rebecka  and  Nyg\r{a}rd, Jan F  and  Dalianis, Hercules},
  title     = {Efficient Encoding of Pathology Reports Using Natural Language Processing},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {778--783},
  abstract  = {In this article we present a system that extracts information from pathology
	reports. The reports are written in Norwegian and contain free text describing
	prostate biopsies. Currently, these reports are manually coded for research and
	statistical purposes by trained experts at the Cancer Registry of Norway where
	the coders extract values for a set of predefined fields that are specific for
	prostate cancer.
	The presented system is rule based and achieves an average F-score of 0.91 for
	the fields Gleason grade, Gleason score, the number of biopsies that contain
	tumor tissue, and the orientation of the biopsies. The system also identifies
	reports that contain ambiguity or other content that should be reviewed by an
	expert. The system shows potential to encode the reports considerably faster,
	with less resources, and similar high quality to the manual encoding.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_100}
}