@inproceedings{harrigian-etal-2023-characterization,
title = "Characterization of Stigmatizing Language in Medical Records",
author = "Harrigian, Keith and
Zirikly, Ayah and
Chee, Brant and
Ahmad, Alya and
Links, Anne and
Saha, Somnath and
Beach, Mary Catherine and
Dredze, Mark",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.28",
doi = "10.18653/v1/2023.acl-short.28",
pages = "312--329",
abstract = "Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.",
}
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<abstract>Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.</abstract>
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%0 Conference Proceedings
%T Characterization of Stigmatizing Language in Medical Records
%A Harrigian, Keith
%A Zirikly, Ayah
%A Chee, Brant
%A Ahmad, Alya
%A Links, Anne
%A Saha, Somnath
%A Beach, Mary Catherine
%A Dredze, Mark
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F harrigian-etal-2023-characterization
%X Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.
%R 10.18653/v1/2023.acl-short.28
%U https://aclanthology.org/2023.acl-short.28
%U https://doi.org/10.18653/v1/2023.acl-short.28
%P 312-329
Markdown (Informal)
[Characterization of Stigmatizing Language in Medical Records](https://aclanthology.org/2023.acl-short.28) (Harrigian et al., ACL 2023)
ACL
- Keith Harrigian, Ayah Zirikly, Brant Chee, Alya Ahmad, Anne Links, Somnath Saha, Mary Catherine Beach, and Mark Dredze. 2023. Characterization of Stigmatizing Language in Medical Records. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 312–329, Toronto, Canada. Association for Computational Linguistics.