CARE-SD: Classifier-based analysis for recognizing and eliminating stigmatizing and doubt marker labels in electronic health records: model development and validation
arxiv(2024)
摘要
Objective: To detect and classify features of stigmatizing and biased
language in intensive care electronic health records (EHRs) using natural
language processing techniques. Materials and Methods: We first created a
lexicon and regular expression lists from literature-driven stem words for
linguistic features of stigmatizing patient labels, doubt markers, and scare
quotes within EHRs. The lexicon was further extended using Word2Vec and GPT
3.5, and refined through human evaluation. These lexicons were used to search
for matches across 18 million sentences from the de-identified Medical
Information Mart for Intensive Care-III (MIMIC-III) dataset. For each
linguistic bias feature, 1000 sentence matches were sampled, labeled by expert
clinical and public health annotators, and used to supervised learning
classifiers. Results: Lexicon development from expanded literature stem-word
lists resulted in a doubt marker lexicon containing 58 expressions, and a
stigmatizing labels lexicon containing 127 expressions. Classifiers for doubt
markers and stigmatizing labels had the highest performance, with macro
F1-scores of .84 and .79, positive-label recall and precision values ranging
from .71 to .86, and accuracies aligning closely with human annotator agreement
(.87). Discussion: This study demonstrated the feasibility of supervised
classifiers in automatically identifying stigmatizing labels and doubt markers
in medical text, and identified trends in stigmatizing language use in an EHR
setting. Additional labeled data may help improve lower scare quote model
performance. Conclusions: Classifiers developed in this study showed high model
performance and can be applied to identify patterns and target interventions to
reduce stigmatizing labels and doubt markers in healthcare systems.
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