ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024
Measuring and Reducing Racial Bias in a Pediatric Urinary Tract Infection Model.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.The Lancet. Digital health · 2026Article
- A Decision-Theoretic Perspective on Fairness in Clinical Predictive Models.Research square · 2026Article
- The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Raising awareness of potential biases in medical machine learning: Experience from a Datathon.PLOS digital health · 2025Article
- 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
- Algorithmic Individual Fairness and Healthcare: A Scoping Review.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
Abstract
Clinical predictive models that include race as a predictor have the potential to exacerbate disparities in healthcare. Such models can be respecified to exclude race or optimized to reduce racial bias. We investigated the impact of such respecifications in a predictive model - UTICalc - which was designed to reduce catheterizations in young children with suspected urinary tract infections. To reduce racial bias, race was removed from the UTICalc logistic regression model and replaced with two new features. We compared the two versions of UTICalc using fairness and predictive performance metrics to understand the effects on racial bias. In addition, we derived three new models for UTICalc to specifically improve racial fairness. Our results show that, as predicted by previously described impossibility results, fairness cannot be simultaneously improved on all fairness metrics, and model respecification may improve racial fairness but decrease overall predictive performance.
Identifiers
38827048PMC11141814What OpenQuestion holds
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.