{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:35:54Z","timestamp":1771266954128,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Stigmatizing language (SL) in Electronic Health Records (EHRs) can perpetuate biases and negatively impact patient care. This study introduces a novel method for automatically detecting such language to improve healthcare documentation practices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We developed a multi-stage transfer learning framework integrating semantic, syntactic, and task adaptation using three datasets: hate speech, clinical phenotypes, and stigmatizing language. Experiments were conducted on stigmatizing language dataset which consists of 4,129 de-identified EHR notes (72.7% stigmatizing, 27.3% non-stigmatizing), split 80\/20 for training and testing. Longformer, BERT, and ClinicalBERT models were evaluated, and model performance was assessed on 35 randomized subsets of the test set (each comprising 70% of test data). The Wilcoxon-Mann-Whitney test was used to evaluate statistical significance, with Bonferroni correction applied to control for multiple hypothesis testing. Baseline models included zero-shot and few-shot GPT-4o, Support Vector Machine, Random Forest, Logistic Regression, and Multinomial Naive Bayes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed framework achieved the highest accuracy, with fully adapted Longformer reaching 89.83%. Performance improvements remained statistically significant after Bonferroni correction compared to all baselines (p &amp;lt; .05). The framework demonstrated robust gains across different stigmatizing language types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study underscores the value of domain-adaptive NLP for detecting stigmatizing language in EHRs. The multi-stage transfer learning framework effectively captures subtle biases often missed by conventional models, enabling more objective and respectful clinical documentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This framework offers a statistically validated, high-performing framework for detecting stigmatizing language in EHRs, supporting responsible AI and promoting equity in clinical care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf193","type":"journal-article","created":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T20:13:00Z","timestamp":1762719180000},"page":"283-294","source":"Crossref","is-referenced-by-count":2,"title":["Automated detection of stigmatizing language in Electronic Health Records (EHRs) using a multi-stage transfer learning approach"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2908-9072","authenticated-orcid":false,"given":"Liyang","family":"Xue","sequence":"first","affiliation":[{"name":"Department of Library and Information Science, School of Communication & Information, Rutgers University , New Brunswick, NJ, 08901,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A M Muntasir","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Library and Information Science, School of Communication & Information, Rutgers University , New Brunswick, NJ, 08901,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0254-3127","authenticated-orcid":false,"given":"Charles R","family":"Senteio","sequence":"additional","affiliation":[{"name":"Department of Library and Information Science, School of Communication & Information, Rutgers University , New Brunswick, NJ, 08901,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8194-2336","authenticated-orcid":false,"given":"Vivek K","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Library and Information Science, School of Communication & Information, Rutgers University , New Brunswick, NJ, 08901,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"2026012716172043400_ocaf193-B1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1080\/01612840802694668","article-title":"Stigmatizing language with unintended meanings: \u201cpersons with mental illness\u201d or \u201cmentally ill persons\u201d?","volume":"30","author":"Shattell","year":"2009","journal-title":"Issues Ment Health Nurs"},{"key":"2026012716172043400_ocaf193-B2","doi-asserted-by":"crossref","first-page":"248","DOI":"10.2105\/AJPH.93.2.248","article-title":"Paved with good intentions: do public health and human service providers contribute to racial\/ethnic disparities in health?","volume":"93","author":"van Ryn","year":"2003","journal-title":"Am J Public Health"},{"key":"2026012716172043400_ocaf193-B3","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1007\/s11606-013-2441-1","article-title":"Physicians and implicit bias: how doctors may unwittingly perpetuate health care disparities","volume":"28","author":"Chapman","year":"2013","journal-title":"J Gen Intern Med"},{"key":"2026012716172043400_ocaf193-B4","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s11606-017-4289-2","article-title":"Do words matter? 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