Early prediction of preeclampsia via machine learning is a research paper published in American Journal of Obstetrics & Gynecology MFM (2020). On theSindex it has a DataRank of 0.743. It has been cited 141 times.
Scored on demand from live citation data
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DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
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Base Score Contribution
0.743
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 HL139844
Stanford Maternal and Child Health Research Institute
Burroughs Wellcome Fund
National Institutes of Health
FWCI
4.79
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 2 of A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors
Additional file 2 of A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors
Additional file 1 of A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors
Additional file 1 of A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors