Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning is a research paper published in Journal of the American Medical Informatics Association (2021). On theSindex it has a DataRank of 1.9. It has been cited 36 times, with 32 citing works in its 1-hop citation network.
Scored on demand from live citation data
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
0.542
From this paper's citation signal
Citation Network Contribution
1.4
From 28 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 32 citers.
National Institutes of Health
Grant: R01LM011829
NIAID NIH HHS
Grant: R01 AI130460
National Institutes of Health
Grant: 1R01AI130460-01
Dynamic learning for post-vaccine event prediction using temporal information in VAERS
National Institutes of Health
Grant: 5R01LM011829-03
Patient Medical History Representation, Extraction, and Inference from EHR Data
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals