FedIMPUTE: Privacy-preserving missing value imputation for multi-site heterogeneous electronic health records.

Siqi Li¹, Mengying Yan², Ruizhi Yuan², Molei Liu³, Nan Liu⁴, Chuan Hong⁵

¹Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.

²Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

³Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA; Department of Biostatistics, Peking University Health Science Center, Peking University, Beijing, China; Beijing International Center for Mathematical Research, Peking University, Beijing, China. Electronic address: ml4890@cumc.columbia.edu.

⁴Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore; Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; NUS Artificial Intelligence Institute, National University of Singapore, Singapore, Singapore. Electronic address: liu.nan@duke-nus.edu.sg.

⁵Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA; Duke Clinical Research Institute, Durham, NC, USA. Electronic address: Chuan.Hong@duke.edu.

PubMed DOI

Abstract

OBJECTIVES: We propose FedIMPUTE, a communication-efficient federated learning (FL) based approach for missing value imputation (MVI). Our method enables multiple sites to collaboratively perform MVI in a privacy-preserving manner, addressing challenges of data-sharing constraints and population heterogeneity. METHODS: We begin by conducting MVI locally at each participating site, followed by the…

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