@inproceedings{moen-etal-2017-detecting,
title = "Detecting mentions of pain and acute confusion in {F}innish clinical text",
author = {Moen, Hans and
Hakala, Kai and
Mehryary, Farrokh and
Peltonen, Laura-Maria and
Salakoski, Tapio and
Ginter, Filip and
Salanter{\"a}, Sanna},
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2347",
doi = "10.18653/v1/W17-2347",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting mentions of pain and acute confusion in Finnish clinical text
%A Moen, Hans
%A Hakala, Kai
%A Mehryary, Farrokh
%A Peltonen, Laura-Maria
%A Salakoski, Tapio
%A Ginter, Filip
%A Salanterä, Sanna
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F moen-etal-2017-detecting
%X 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.
%R 10.18653/v1/W17-2347
%U https://aclanthology.org/W17-2347
%U https://doi.org/10.18653/v1/W17-2347
%P 365-372
Markdown (Informal)
[Detecting mentions of pain and acute confusion in Finnish clinical text](https://aclanthology.org/W17-2347) (Moen et al., BioNLP 2017)
ACL
- Hans Moen, Kai Hakala, Farrokh Mehryary, Laura-Maria Peltonen, Tapio Salakoski, Filip Ginter, and Sanna Salanterä. 2017. Detecting mentions of pain and acute confusion in Finnish clinical text. In BioNLP 2017, pages 365–372, Vancouver, Canada,. Association for Computational Linguistics.