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DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

A lossless, one-shot and privacy-preserving distributed algorithm was revealed for fitting linear mixed models on multi-site data. The algorithm was applied to a study of 120,609 COVID-19 patients using only minimal aggregated data from each of 14 sites.

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Principais autores: Chongliang Luo, Md. Nazmul Islam, Natalie E. Sheils, John Buresh, Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Mackenzie Edmondson, Rui Duan, Jiayi Tong, Arielle Marks-Anglin, Jiang Bian, Zhaoyi Chen, Talita Duarte-Salles, Sergio Fernández-Bertolín, Thomas Falconer, Chungsoo Kim, Rae Woong Park, Stephen R. Pfohl, Nigam H. Shah, Andrew E. Williams, Hua Xu, Yujia Zhou, Ebbing Lautenbach, Jalpa A. Doshi, Rachel M. Werner, David A. Asch, Yong Chen
Formato: Artigo
Idioma:Inglês
Publicado em: Nature Portfolio 2022-03-01
coleção:Nature Communications
Acesso em linha:https://doi.org/10.1038/s41467-022-29160-4
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