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Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative

Hongbo Chen¹, Myrtede Alfred¹, Eldan Cohen¹

¹Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.

JMIR Medical Informatics

|August 18, 2025

Summary

This summary is machine-generated.

In-context learning (ICL) effectively detects stigmatizing language in electronic health records (EHRs) with less data. This data-efficient approach also shows improved fairness compared to traditional methods.

Keywords:

artificial intelligence electronic health record fairness few-shot in-context learning large language model machine learning prompting strategy stigmatizing language text classification zero-shot

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Area of Science:

Natural Language Processing (NLP)
Machine Learning in Healthcare
Clinical Informatics

Background:

Stigmatizing language in electronic health records (EHRs) perpetuates bias and risks patient care.
Supervised machine learning models for stigma detection require extensive annotated datasets.
In-context learning (ICL) offers a data-efficient alternative using instructions and examples.

Purpose of the Study:

To evaluate the effectiveness of ICL for detecting stigmatizing language in EHRs.
To assess ICL performance under data-scarce conditions.
To compare ICL with traditional machine learning approaches regarding efficiency and fairness.

Main Methods:

Analysis of 5043 sentences from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset.
Comparison of ICL (zero-shot, few-shot with 4 prompting strategies) against zero-shot textual entailment and few-shot SetFit models.
Evaluation of model fairness using the equal performance criterion across protected attributes (sex, age, race).

Main Results:

ICL models, like GEMMA-2 (zero-shot) and LLAMA-3 (few-shot), significantly outperformed textual entailment and SetFit models in detecting stigmatizing language.
The best ICL model achieved an F1-score of 0.901 with only 32 labeled instances, closely rivaling supervised fine-tuning models trained on thousands of instances.
ICL models demonstrated greater fairness, exhibiting less bias across protected attributes compared to supervised fine-tuning models.

Conclusions:

ICL provides a flexible and robust method for identifying stigmatizing language in EHRs.
ICL is a more data-efficient and equitable alternative to conventional supervised machine learning.
ICL can improve bias detection in clinical documentation while minimizing the need for large labeled datasets.