Synthetic data in machine learning for medicine and healthcare is a research paper published in Nature Biomedical Engineering (2021). On theSindex it has a DataRank of 0.988. It has been cited 724 times.
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Linked data & code
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Base Score Contribution
0.988
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 →U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences
Grant: R35GM138216
NHGRI NIH HHS
Grant: T32 HG002295
National Institutes of Health
Grant: 5R35GM138216-03
Interpretable Deep Learning Algorithms for Pathology Image Analysis
FWCI
25.97
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility
Additional file 1 of Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility
Additional file 2 of Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility
Additional file 2 of Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility