Detecting mentions of pain and acute confusion in Finnish clinical text

Hans Moen, Kai Hakala, Farrokh Mehryary, Laura-Maria Peltonen, Tapio Salakoski, Filip Ginter, Sanna Salanterä


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.
Anthology ID:
W17-2347
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
365–372
Language:
URL:
https://www.aclweb.org/anthology/W17-2347
DOI:
10.18653/v1/W17-2347
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2347.pdf