Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes is a research paper published in medRxiv (2020). On theSindex it has a DataRank of 0. It has been cited 1 time.
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?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Institutes of Health
Grant: 5R01HL153646-04
Blockade of calcium channels and beta adrenergic receptors for physiologic abnormalities in heart failure with preserved ejection fraction (BLOCK HFpEF)
National Institutes of Health
Grant: 1K99LM012926-01A1
Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records
National Institutes of Health
Grant: 1K23HL133843-01
Management of hypertension in obesity: Antihypertensive class effects, blood pressure control, and renal and cardiac outcomes
National Institutes of Health
Grant: 5R01LM010098-14
Bioinformatics Strategies for Genome Wide Association Studies
Fields of Study
Keywords
Sustainable Development Goals