Semantic Scholar Open Access 2023 26 sitasi

Characterization of Stigmatizing Language in Medical Records

Keith Harrigian Ayah Zirikly Brant Chee Alya Ahmad A. Links +3 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (8)

Keith Harrigian

Ayah Zirikly

Brant Chee

Alya Ahmad

A. Links

S. Saha

M. Beach

M. Dredze

Format Sitasi

APA MLA BibTeX

Harrigian, K., Zirikly, A., Chee, B., Ahmad, A., Links, A., Saha, S. et al. (2023). Characterization of Stigmatizing Language in Medical Records. https://doi.org/10.18653/v1/2023.acl-short.28

Akses Cepat

Lihat di Sumber doi.org/10.18653/v1/2023.acl-short.28

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 26×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/2023.acl-short.28
Akses: Open Access ✓