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

ABSTRACT:

Objective

To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).

Materials and methods

We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset. Annotators labeled for exemplars of 5 stigmatizing and 1 positive/preferred language categories. We used a semantic similarity-based search approach to expand the initial dataset by adding additional exemplars, composing an enhanced dataset. We employed traditional classifiers (Support Vector Machine, Decision Trees, and Random Forest) and a transformer-based model, ClinicalBERT (Bidirectional Encoder Representations from Transformers) and BERT base. Models were tra

SUBMITTER: Scroggins JK 

PROVIDER: S-EPMC11756426 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature