Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing
ClinicalBERT effectively captures the nuanced stigmatizing language in obstetric clinical notes. Our semantic similarity-based search approach to rapidly extract additional exemplars enhanced the performances while reducing the need for labor-intensive annotation.
Authors
- Jihye Kim Scroggins (ORCID: https://orcid.org/0000-0002-0864-7867)
- Maxim Topaz (ORCID: https://orcid.org/0000-0002-2358-9837)
- Hans Jonas Fossum Moen (ORCID: https://orcid.org/0000-0003-1418-7892)
- Anahita Davoudi (ORCID: https://orcid.org/0000-0003-4345-3889)
- Kenrick Cato (ORCID: https://orcid.org/0000-0002-0704-3826)
- Danielle Scharp (ORCID: https://orcid.org/0000-0002-3265-6667)
- Ismael Ibrahim Hulchafo (ORCID: https://orcid.org/0009-0005-5504-4646)
- Veronica Barcelona (ORCID: https://orcid.org/0000-0003-3070-1716)
- Sarah Harkins (ORCID: https://orcid.org/0000-0003-2528-7186)
- Michele Tadiello (ORCID: https://orcid.org/0009-0004-5351-095X)
Institutions
- Columbia University Irving Medical Center (US)
- Columbia University (US)
- University of Pennsylvania (US)
- Icahn School of Medicine at Mount Sinai (US)
- Aalto University (FI)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1093/jamia/ocae290
- Citations
- 16
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 11.05
Funders
- Gordon and Betty Moore Foundation