Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database is a dataset published in Seminars in Arthritis and Rheumatism (2022). On theSindex it has a DataRank of 1.5, placing it in the top 13.6% of the data-sharing corpus. It has been cited 27 times, with 19 citing works in its 1-hop citation network.
Ranks in the top 14% for downstream scientific impact
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?
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Base Score Contribution
0.500
From this paper's citation signal
Citation Network Contribution
1.0
From 14 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 19 citers.
NHLBI NIH HHS
Grant: N01AR22258
NIAMS NIH HHS
Grant: N01AR22260
NIAMS NIH HHS
Grant: N01AR22261
National Institute for Health Research (NIHR)
Grant: NF-SI-0611-10031
NIAMS NIH HHS
Grant: N01AR22259
NIAMS NIH HHS
Grant: N01AR22262
Novartis Institutes for BioMedical Research Basel
National Institute for Health Research (NIHR)
Department of Health
FWCI
3.69
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach
Additional file 1 of Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach