Annotating and Detecting Medical Events in Clinical Notes

Prescott Klassen; Fei Xia; Meliha Yetisgen-Yildiz

Annotating and Detecting Medical Events in Clinical Notes

Prescott Klassen, Fei Xia, Meliha Yetisgen

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Abstract

Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7% and 91.8%, respectively.

Anthology ID:: L16-1545
Volume:: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:: May
Year:: 2016
Address:: Portorož, Slovenia
Editors:: Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:: LREC
SIG:
Publisher:: European Language Resources Association (ELRA)
Note:
Pages:: 3417–3421
Language:
URL:: https://aclanthology.org/L16-1545/
DOI:
Bibkey:
Cite (ACL):: Prescott Klassen, Fei Xia, and Meliha Yetisgen. 2016. Annotating and Detecting Medical Events in Clinical Notes. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3417–3421, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):: Annotating and Detecting Medical Events in Clinical Notes (Klassen et al., LREC 2016)
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PDF:: https://aclanthology.org/L16-1545.pdf

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@inproceedings{klassen-etal-2016-annotating,
    title = "Annotating and Detecting Medical Events in Clinical Notes",
    author = "Klassen, Prescott  and
      Xia, Fei  and
      Yetisgen, Meliha",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Grobelnik, Marko  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, Helene  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L16-1545/",
    pages = "3417--3421",
    abstract = "Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7{\%} and 91.8{\%}, respectively."
}

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%0 Conference Proceedings
%T Annotating and Detecting Medical Events in Clinical Notes
%A Klassen, Prescott
%A Xia, Fei
%A Yetisgen, Meliha
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F klassen-etal-2016-annotating
%X Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7% and 91.8%, respectively.
%U https://aclanthology.org/L16-1545/
%P 3417-3421

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Markdown (Informal)

[Annotating and Detecting Medical Events in Clinical Notes](https://aclanthology.org/L16-1545/) (Klassen et al., LREC 2016)

Annotating and Detecting Medical Events in Clinical Notes (Klassen et al., LREC 2016)

ACL

Prescott Klassen, Fei Xia, and Meliha Yetisgen. 2016. Annotating and Detecting Medical Events in Clinical Notes. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3417–3421, Portorož, Slovenia. European Language Resources Association (ELRA).