@inproceedings{yim-etal-2019-automatic,
title = "Automatic rubric-based content grading for clinical notes",
author = "Yim, Wen-wai and
Mills, Ashley and
Chun, Harold and
Hashiguchi, Teresa and
Yew, Justin and
Lu, Bryan",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6216",
doi = "10.18653/v1/D19-6216",
pages = "126--135",
abstract = "Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.",
}
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<abstract>Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.</abstract>
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%0 Conference Proceedings
%T Automatic rubric-based content grading for clinical notes
%A Yim, Wen-wai
%A Mills, Ashley
%A Chun, Harold
%A Hashiguchi, Teresa
%A Yew, Justin
%A Lu, Bryan
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F yim-etal-2019-automatic
%X Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.
%R 10.18653/v1/D19-6216
%U https://aclanthology.org/D19-6216
%U https://doi.org/10.18653/v1/D19-6216
%P 126-135
Markdown (Informal)
[Automatic rubric-based content grading for clinical notes](https://aclanthology.org/D19-6216) (Yim et al., Louhi 2019)
ACL
- Wen-wai Yim, Ashley Mills, Harold Chun, Teresa Hashiguchi, Justin Yew, and Bryan Lu. 2019. Automatic rubric-based content grading for clinical notes. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 126–135, Hong Kong. Association for Computational Linguistics.