ArticleJournal of the American Medical Informatics Association : JAMIA2021
Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.Journal of racial and ethnic health disparities · 2026Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction.JAMIA open · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.JAMIA open · 2026Review
- A fairness framework for natural language processing in substance use disorders and overdose.PLOS digital health · 2026Article
- Performance fairness of neural network models in early risk assessment of inpatients with varying severity: a retrospective study.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Artificial intelligence for alcohol, opioid, and cannabis use disorders screening and management: a narrative review of barriers and facilitators to clinical implementation.Frontiers in digital health · 2026Review
- Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults.medRxiv : the preprint server for health sciences · 2025Article
- A community-based approach to ethical decision-making in artificial intelligence for health care.JAMIA open · 2025Review
- Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.PLOS digital health · 2025Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Identifying and mitigating algorithmic bias in the safety net.NPJ digital medicine · 2025Article
- Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.Nature medicine · 2025Observational
- Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study.BioData mining · 2025Article
- Article
- Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists.Clinical epidemiology · 2025Review
- Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo assess fairness and bias of a previously validated machine learning opioid misuse classifier. MATERIALS &
methodsTwo experiments were conducted with the classifier's original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier's predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics.
resultsWe identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included "heroin" and "substance abuse" across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05). DISCUSSION: The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed.
conclusionStandardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.