Health AI Central
Peer-Reviewed Publication
Npj Health Syst2025;2(1)August 12, 2025Journal Article

FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest outcome prediction.

Siqi Li1, Qiming Wu1, Doudou Zhou2, Xin Li1, Di Miao1, Chuan Hong3, Wenjun Gu1, Yuqing Shang1, Yohei Okada4,5, Michael Hao Chen1, Mengying Yan3, Yilin Ning1, Marcus Eng Hock Ong6,7, Nan Liu8,9,10
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
2Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore.
3Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
4Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
5Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
6Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
7Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore.
8Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore. liu.nan@duke-nus.edu.sg.
9Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore. liu.nan@duke-nus.edu.sg.
10NUS Artificial Intelligence Institute, National University of Singapore, Singapore, Singapore. liu.nan@duke-nus.edu.sg.
PubMed PMC DOI PDF

Abstract

Health equity is a critical concern in clinical research and practice, as biased predictive models can exacerbate disparities in clinical decision-making and patient outcomes. As healthcare systems increasingly rely on data-driven models, ensuring fairness in these systems is essential to prevent perpetuating existing disparities. While large-scale healthcare data exists across multiple institutio…

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