Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study.

ABSTRACT:

Background

The presence of stigmatizing language within electronic health records (EHRs) poses significant risks to patient care by perpetuating biases. While numerous studies have explored the use of supervised machine learning models to detect stigmatizing language automatically, these models require large, annotated datasets, which may not always be readily available. In-context learning (ICL) has emerged as a data-efficient alternative, allowing large language models to adapt to tasks using only instructions and examples.

Objective

We aimed to investigate the efficacy of ICL in detecting stigmatizing language within EHRs under data-scarce conditions.

Methods

We analyzed 5043 sentences from the Medical Information Mart for Intensive Care-IV dataset, which contains

SUBMITTER: Chen H 

PROVIDER: S-EPMC12402740 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature