Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing

Scroggins, J. K., Hulchafo, I. I., Harkins, S., Scharp, D., Moen, H., Davoudi, A., Cato, K., Tadiello, M., Topaz, M., & Barcelona, V. (2024). Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae290
Authors:
Jihye Kim Scroggins
Ismael Ibrahim Hulchafo
Sarah Harkins
Danielle Scharp
Hans Moen
Anahita Davoudi
Kenrick Cato
Michele Tadiello
Maxim Topaz
Veronica Barcelona
Affiliated Authors:
Jihye Kim Scroggins
Ismael Ibrahim Hulchafo
Sarah Harkins
Michele Tadiello
Maxim Topaz
Veronica Barcelona
Columbia Affiliation:
Subjects:
Natural Language Processing (MeSH)
Electronic Health Records (MeSH)
Emergency Department Crowding (OpenAlex Topic)
Impact of Language Interpretation in Healthcare (OpenAlex Topic)
Deep Learning Applications in Healthcare (OpenAlex Topic)
Author Keywords:
bias
electronic health records
health communication
natural language processing
nursing informatics
elink
Publication Type:
Article
Unique ID:
10.1093/jamia/ocae290
PMID:
39569431
DOI:
10.1093/jamia/ocae290
Journal:
Journal of the American Medical Informatics Association
Publication Date:
Data Source:
OpenAlex
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