Federated learning for predicting clinical outcomes in patients with COVID-19 is a research paper published in Nature Medicine (2021). On theSindex it has a DataRank of 0.990. It has been cited 735 times.
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Base Score Contribution
0.990
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →NHLBI NIH HHS
Grant: R01 HL141237
Intramural NIH HHS
Grant: ZID BC011242
NLM NIH HHS
Grant: R01 LM013151
NHLBI NIH HHS
Grant: R42 HL145669
Cancer Research UK
Grant: 26884
Engineering and Physical Sciences Research Council
Grant: EP/N014588/1
NCI NIH HHS
Grant: P30 CA008748
Intramural NIH HHS
Grant: ZIA CL040015
National Institute for Health Research (NIHR)
Grant: NF-SI-0515-10067
National Institutes of Health
Grant: 1ZIACL040015-04
Interventional Oncology
National Institutes of Health
Grant: 1ZIDBC011242-11
Center for Interventional Oncology
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources
Additional file 1 of Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources
Additional file 1 of An ensemble model for predicting dispositions of emergency department patients
Additional file 1 of An ensemble model for predicting dispositions of emergency department patients