Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy is a research paper published in JACC. Clinical electrophysiology (2021). On theSindex it has a DataRank of 1.1. It has been cited 23 times, with 22 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
Repositories this paper deposited data in (declared in PubMed).
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Base Score Contribution
0.477
From this paper's citation signal
Citation Network Contribution
0.595
From 20 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 22 citers.
NHLBI
Grant: HL118277
American Heart Association-American Stroke Association
Grant: 17GRNT33670428
NHLBI NIH HHS
Grant: R56 HL118277
NHLBI NIH HHS
Grant: R01 HL118277
Oregon Health and Science University
National Institutes of Health
Boston Scientific Corporation
FWCI
2.63
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features
Additional file 1 of Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features