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A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data
Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844867/ https://www.ncbi.nlm.nih.gov/pubmed/36649349 http://dx.doi.org/10.1371/journal.pone.0280192 |
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author | Yan, Zhiyu Zachrison, Kori S. Schwamm, Lee H. Estrada, Juan J. Duan, Rui |
author_facet | Yan, Zhiyu Zachrison, Kori S. Schwamm, Lee H. Estrada, Juan J. Duan, Rui |
author_sort | Yan, Zhiyu |
collection | PubMed |
description | Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation resource requirements, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data while accounting for site-level heterogeneity. Fed-GLMM can be applied to both federated and centralized research networks to enable privacy-preserving data integration and improve computational efficiency. By communicating a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted to the pooled dataset. We demonstrate the performance of Fed-GLMM in numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities. |
format | Online Article Text |
id | pubmed-9844867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98448672023-01-18 A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data Yan, Zhiyu Zachrison, Kori S. Schwamm, Lee H. Estrada, Juan J. Duan, Rui PLoS One Research Article Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation resource requirements, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data while accounting for site-level heterogeneity. Fed-GLMM can be applied to both federated and centralized research networks to enable privacy-preserving data integration and improve computational efficiency. By communicating a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted to the pooled dataset. We demonstrate the performance of Fed-GLMM in numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities. Public Library of Science 2023-01-17 /pmc/articles/PMC9844867/ /pubmed/36649349 http://dx.doi.org/10.1371/journal.pone.0280192 Text en © 2023 Yan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yan, Zhiyu Zachrison, Kori S. Schwamm, Lee H. Estrada, Juan J. Duan, Rui A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
title | A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
title_full | A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
title_fullStr | A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
title_full_unstemmed | A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
title_short | A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
title_sort | privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844867/ https://www.ncbi.nlm.nih.gov/pubmed/36649349 http://dx.doi.org/10.1371/journal.pone.0280192 |
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