Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
January 17, 2023PLoS ONEOpen Access

A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data

View Full Paper
Ask AI
Bookmark
Share

Authors

ZYZhiyu YanHarvard UniversityKZKori S. ZachrisonNorthwestern University
Lee H. Schwamm
Lee H. SchwammYale University

Discussion

Loading...

Member takes

Implication

Key Points

Key points are not available for this paper at this time.

Cite This Study

Yan et al. (2023) studied this question.

synapsesocial.com/papers/6a9e4c0246bb483cebf6cddahttps://doi.org/10.1371/journal.pone.0280192
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease2022 · 438 citations
  2. 2Prevalence of COVID-19-related risk factors and risk of severe influenza outcomes in cancer survivors: A matched cohort study using linked English electronic health records data2020 · 61 citations
  3. 3Healthcare Data Integration and Informatics in the Cloud2015 · 33 citations
  4. 4The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future2013 · 743 citations
  5. 5Targeting underrepresented populations in precision medicine: A federated transfer learning approach2023 · 53 citations