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.

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.

    Related Posts

    Selected Papers

    Intersectional and Marginal Debiasing in Prediction Models for Emergency Admissions
    Elle Lett, Shakiba Shahbandegan, Yuval Barak-Corren, Andrew M. Fine, William G. La Cava (2025)
    JAMA Network Open
    doi.org  |  PubMed  |  pdf  |  code
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    Preprint
    pdf
    Translating intersectionality to fair machine learning in health sciences
    Elle Lett and William G. La Cava (2023)
    Nature Machine Intelligence
    nature.com  |  pdf  |  blog