Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN) is a research paper published in Computer Methods and Programs in Biomedicine Update (2021). On theSindex it has a DataRank of 0. It has been cited 19 times.
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.
We only score data papers we can read in full — never from an abstract alone.
National Institutes of Health
Grant: R01AI141810
National Institutes of Health
Grant: R01AI145552
National Institutes of Health
Grant: R01CA246418
National Institutes of Health
Grant: R21AG068717
National Institutes of Health
Grant: R21AI138815
National Institutes of Health
Grant: R21CA245858
National Institutes of Health
Grant: U18DP006512
National Institutes of Health
Grant: 5R21AI138815-02
HIV Dynamic Modelling for Identification of Transmission Epicenters (HIV-DYNAMITE)
National Institutes of Health
Grant: 5U18DP006512-03
Using Real-world Data to Assess the Burden of Diabetes in Children and Adolescents in Florida
National Institutes of Health
Grant: 1R01AI141810-01
Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents
National Institutes of Health
Grant: 5R01AI145552-02
Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework
National Institutes of Health
Grant: 3R21CA245858-01A1S1
Examine the risk of Alzheimer's disease in sexual and gender minorities
National Institutes of Health
Grant: 1R21AG068717-01
Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical Trials
National Institutes of Health
Grant: 5R01CA246418-04
The benefits and harms of lung cancer screening in Florida
Fields of Study
Keywords
Sustainable Development Goals
Infant Health and Development Program (IHDP): Enhancing the Outcomes of Low Birth Weight, Premature Infants in the United States, 1985-1988
Additional file 1 of Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?
Additional file 1 of Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?