Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation is a research paper published in medRxiv (2024). On theSindex it has a DataRank of 0.208. It has been cited 3 times.
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Base Score Contribution
0.208
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 →National Institutes of Health
Grant: 5K23HL169839-03
Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation
National Institutes of Health
Grant: 1R01HL139731-01
Genomics of Cardiac Arrhythmias
National Institutes of Health
Grant: 1R01HL157635-01A1
Using Electrocardiogram Genetics to Inform Arrhythmia Risk
European Commission
Grant: 965286
Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation
National Institutes of Health
Grant: 5K08HL161448-04
Integrating genomic and nongenomic risk for coronary artery disease
National Institutes of Health
Grant: 1R01EY016462-01A1
Quantitative RNFL Assessment for Glaucoma Diagnosis
National Institutes of Health
Grant: 5R01HL092577-02
Identification of common genetic variants for atrial fibrillation and PR interval
NHLBI NIH HHS
Grant: R01 HL139731
NHLBI NIH HHS
Grant: R01 HL092577
NHLBI NIH HHS
Grant: R01 HL157635
NHLBI NIH HHS
Grant: K08 HL161448
NHLBI NIH HHS
Grant: K23 HL169839
Fields of Study
Keywords
Sustainable Development Goals