ArticlePloS one2023
A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- A communication-efficient federated learning algorithm to assess racial disparities in post-transplantation survival time.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.NPJ digital medicine · 2025Article
- Neighborhood Deprivation and Racial Disparities in Heart Failure Outcomes: A Counterfactual Approach.JACC. Advances · 2025Article
- Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.Transfusion · 2025Review
- FedGMMAT: Federated generalized linear mixed model association tests.PLoS computational biology · 2024Article
- AI and Technology Enabled Clinical Workflow Redesign.Telemedicine reports · 2024Article
- Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Multi-Task Learning with Summary Statistics.Advances in neural information processing systems · 2023Article
- Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.