Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes

Wu, Q., Schuemie, M. J., Suchard, M. A., Ryan, P., Hripcsak, G. M., Rohde, C. A., & Chen, Y. (2023). Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes. Journal of Biomedical Informatics, 145, 104476. https://doi.org/10.1016/j.jbi.2023.104476
Authors:
Qiong Wu
Martijn J. Schuemie
Marc A. Suchard
Patrick Ryan
George Hripcsak
Charles A. Rohde
Yong Chen
Affiliated Authors:
Patrick Ryan
George Hripcsak
Columbia Affiliation:
Subjects:
Algorithms (MeSH)
Machine Learning (MeSH)
Methods for Handling Missing Data in Statistical Analysis (OpenAlex Topic)
Methods for Evidence Synthesis in Research (OpenAlex Topic)
Statistical Methods in Clinical Trials and Drug Development (OpenAlex Topic)
Author Keywords:
distributed algorithm
distributed research networks
evidence synthesis
padé approximants
rare outcomes
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Publication Type:
Article
Unique ID:
10.1016/j.jbi.2023.104476
PMID:
37598737
DOI:
10.1016/j.jbi.2023.104476
Journal:
Journal of Biomedical Informatics
Publication Date:
Data Source:
OpenAlex
Source Link:
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