Deep propensity network using a sparse autoencoder for estimation of treatment effects is a research paper published in Journal of the American Medical Informatics Association (2020). On theSindex it has a DataRank of 0. It has been cited 15 times.
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NIH
Grant: R01AI145552
NIH
Grant: R01CA246418
NIH
Grant: U18DP006512
NIH
Grant: R21AG068717
NIH
Grant: R21CA245858
National Institutes of Health
Grant: 3R21CA245858-01A1S1
Examine the risk of Alzheimer's disease in sexual and gender minorities
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: 5U18DP006512-03
Using Real-world Data to Assess the Burden of Diabetes in Children and Adolescents in Florida
National Institutes of Health
Grant: 1R21AG068717-01
Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical Trials
FWCI
1.40
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
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?