@inproceedings{cotik-etal-2016-negation,
title = "Negation Detection in Clinical Reports Written in {G}erman",
author = "Cotik, Viviana and
Roller, Roland and
Xu, Feiyu and
Uszkoreit, Hans and
Budde, Klemens and
Schmidt, Danilo",
editor = "Ananiadou, Sophia and
Batista-Navarro, Riza and
Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Thompson, Paul",
booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-5113",
pages = "115--124",
abstract = "An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that are negated or just speculated. The system has been applied to two different types of German clinical texts: clinical notes and discharge summaries. Our approach is built on top of NegEx, a well known algorithm for identifying non-factive mentions of medical findings. In this work, we adjust a previous adaptation of NegEx to German and evaluate the system on our data to detect negation and speculation. The results are compared to a baseline algorithm and are analyzed for both types of clinical documents. Our system achieves an F1-Score above 0.9 on both types of reports.",
}
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<abstract>An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that are negated or just speculated. The system has been applied to two different types of German clinical texts: clinical notes and discharge summaries. Our approach is built on top of NegEx, a well known algorithm for identifying non-factive mentions of medical findings. In this work, we adjust a previous adaptation of NegEx to German and evaluate the system on our data to detect negation and speculation. The results are compared to a baseline algorithm and are analyzed for both types of clinical documents. Our system achieves an F1-Score above 0.9 on both types of reports.</abstract>
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%0 Conference Proceedings
%T Negation Detection in Clinical Reports Written in German
%A Cotik, Viviana
%A Roller, Roland
%A Xu, Feiyu
%A Uszkoreit, Hans
%A Budde, Klemens
%A Schmidt, Danilo
%Y Ananiadou, Sophia
%Y Batista-Navarro, Riza
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Thompson, Paul
%S Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F cotik-etal-2016-negation
%X An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that are negated or just speculated. The system has been applied to two different types of German clinical texts: clinical notes and discharge summaries. Our approach is built on top of NegEx, a well known algorithm for identifying non-factive mentions of medical findings. In this work, we adjust a previous adaptation of NegEx to German and evaluate the system on our data to detect negation and speculation. The results are compared to a baseline algorithm and are analyzed for both types of clinical documents. Our system achieves an F1-Score above 0.9 on both types of reports.
%U https://aclanthology.org/W16-5113
%P 115-124
Markdown (Informal)
[Negation Detection in Clinical Reports Written in German](https://aclanthology.org/W16-5113) (Cotik et al., 2016)
ACL
- Viviana Cotik, Roland Roller, Feiyu Xu, Hans Uszkoreit, Klemens Budde, and Danilo Schmidt. 2016. Negation Detection in Clinical Reports Written in German. In Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016), pages 115–124, Osaka, Japan. The COLING 2016 Organizing Committee.