Reducing health disparities in clinical decision support with fair machine learning

We are developing machine learning algorithms that can adapt to changing hospital environments in real time and make predictions that are equally accuracy among patient subpopulations. We are studying these algorithms for patient admission risk predictions in emergency rooms.

Code

PMCBoost: Proportional Multicalibration Boosting

Interfair: Intersectional Fairness using FOMO

Press

• 🎉 La Cava and Lett’s fair ML tool, Interfair, won first place in the 2023 NIH Challenge, Bias Detection Tools for Clinical Decision Making.

Funding

NIH National Library of Medicine

NCATS

Selected Papers

Intersectional consequences for marginal fairness in prediction models of emergency admissions

Elle Lett, Shakiba Shahbandegan, Yuval Barak-Corren, Andrew Fine, William G La Cava (2024)

Preprint

medrxiv.org  |  medRxiv  |  pdf  |  code

Fair admission risk prediction with proportional multicalibration

William G. La Cava, Elle Lett, Guangya Wan (2023)

Proceedings of the Conference on Health, Inference, and Learning

proceedings.mlr.press  |  pdf  |  code  |  Best Paper Award

Translating intersectionality to fair machine learning in health sciences

Elle Lett, William G. La Cava (2023)

Nature Machine Intelligence

nature.com  |  pdf