Health AI Central
Peer-Reviewed Publication
Artif Intell Med2025;164103130.June 1, 2025Journal Article

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.

Matthew Engelhard1, Daniel Wojdyla2, Haoyuan Wang3, Michael Pencina4, Ricardo Henao4
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, United States of America; Duke AI Health, United States of America. Electronic address: m.engelhard@duke.edu.
2Duke Clinical Research Institute, United States of America.
3Department of Biostatistics and Bioinformatics, Duke University School of Medicine, United States of America.
4Department of Biostatistics and Bioinformatics, Duke University School of Medicine, United States of America; Duke AI Health, United States of America; Duke Clinical Research Institute, United States of America.
PubMed PMC DOI PDF

Abstract

A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical literature to correct such disparities by removing race as a predictor (i.e., race-free models). Alternatively, a variety of machine learning methods…

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