A systematic review of GAN-based longitudinal data imputation that categorizes methods and shows that most ignore missingness mechanisms, static features, and mixed data types.
A joint learning method for incomplete and imbal- anced data in electronic health record based on generative adversarial networks
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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification
A systematic review of GAN-based longitudinal data imputation that categorizes methods and shows that most ignore missingness mechanisms, static features, and mixed data types.