Characterization of Stigmatizing Language in Medical Records

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. The first step to addressing the presence of
stigmatizing language in medical records is identifying it and characterizing
its impact. Towards this end, we release a suite of neural and non-neural
classifiers trained in a supervised manner to recognize three types of
stigmatizing language found in discharge notes from the MIMIC-IV dataset. We
also release the set of 5,043 annotations from 4,710 notes (4,259 patients)
used to train and evaluate our models. These resources provide a foundation
for NLP researchers to contribute timely insights to a problem domain brought
to the forefront by recent legislation regarding clinical documentation
transparency.