@InProceedings{moen-EtAl:2017:BioNLP17,
  author    = {Moen, Hans  and  Hakala, Kai  and  Mehryary, Farrokh  and  Peltonen, Laura-Maria  and  Salakoski, Tapio  and  Ginter, Filip  and  Salanter\"{a}, Sanna},
  title     = {Detecting mentions of pain and acute confusion in Finnish clinical text},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {365--372},
  abstract  = {We study and compare two different approaches to the task of automatic
	assignment of predefined classes to clinical free-text narratives. In the first
	approach this is treated as a traditional mention-level named-entity
	recognition task, while the second approach treats it as a sentence-level
	multi-label classification task. Performance comparison across these two
	approaches is conducted in the form of sentence-level evaluation and
	state-of-the-art methods for both approaches are evaluated. The experiments are
	done on two data sets consisting of Finnish clinical text, manually annotated
	with respect to the topics pain and acute confusion. Our results suggest that
	the mention-level named-entity recognition approach outperforms sentence-level
	classification overall, but the latter approach still manages to achieve the
	best prediction scores on several annotation classes.},
  url       = {http://www.aclweb.org/anthology/W17-2347}
}