A new perspective on how this social theory relates to fair machine learning.
Reducing health disparities in clinical decision support with machine learning
We are developing algorithms that can adapt to changing hospital environments in real time and make predictions that are equally accurate among patient subpopulations. We investigate these algorithms for clinical decision support in emergency medicine and other fields.

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🎉 La Cava and Lett’s fair ML tool, Interfair, won first place in the 2023 NIH Challenge, Bias Detection Tools for Clinical Decision Making.
Related Posts
Selected Papers
Intersectional and Marginal Debiasing in Prediction Models for Emergency Admissions
JAMA Network Open
Deciphering the influence of demographic factors on the treatment of pediatric patients in the emergency department
Pacific Symposium on Biocomputing (PSB)
The Benefit of the Doubt Phenomenon in Emergency Triage Assignment Disparities
Preprint
Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
Preprint
Fair admission risk prediction with proportional multicalibration
Proceedings of Machine Learning Research (PMLR) · Conference on Health, Inference, and Learning (CHIL)