Reducing Racial Disparities in Stroke Thrombolysis Using Fairness-Aware Machine Learning.
TL;DR
A fairness-aware machine learning framework using counterfactual adjustment to account for historical inequities embedded in clinical data reduced treatment disparities by 64.8% without compromising predictive accuracy (AUC = 0.89).
Abstract
Racial and ethnic disparities in the administration of intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) remain persistent, raising concerns that bias-both systemic and algorithmic-may influence treatment decisions. While the NIH Stroke Scale (NIHSS) is intended as an objective tool for assessing stroke severity, its application may not be uniform across patient groups. Using differential item functioning (DIF) analysis on a national inpatient cohort of 983,785 patients, we found that minority patients required higher NIHSS scores than White patients to receive IVT, indicating potential bias in score interpretation. To address this, we applied a fairness-aware machine learning framework using counterfactual adjustment to account for historical inequities embedded in clinical data. This approach reduced treatment disparities by 64.8% without compromising predictive accuracy (AUC = 0.89). Our findings illustrate how standardized tools can contribute to inequity and demonstrate the potential of ethical AI to mitigate disparities in real-world stroke care.