Feasibility of Prioritizing Drug–Drug-Event Associations Found in Electronic Health Records is a research paper published in Drug Safety (2015). On theSindex it has a DataRank of 2.2. It has been cited 48 times, with 41 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
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Base Score Contribution
0.584
From this paper's citation signal
Citation Network Contribution
1.6
From 30 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 41 citers.
U.S. National Library of Medicine
Grant: R01 LM011369
National Institute of General Medical Sciences
Grant: R01 GM101430
National Human Genome Research Institute
Grant: U54 HG004028
NLM NIH HHS
Grant: R01 LM006910
National Institutes of Health
Grant: 5R01GM101430-02
Mining health data for drug safety profiles
National Institutes of Health
Grant: 5R01LM011369-07
From enrichment to insights
National Institutes of Health
Grant: 2U54HG004028-06
Core 5
FWCI
2.75
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
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Additional file 1 of Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness
Additional file 2 of Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness
Additional file 2 of Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness
Additional file 3 of Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness
Additional file 3 of Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness
Additional file 5 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 5 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 6 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 6 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 1 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 3 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 1 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 2 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 4 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 2 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 3 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
Additional file 4 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes