  Electronic Health Records often suffer from missing data, which poses a major
problem in clinical practice and clinical studies. A novel approach for dealing
with missing data are Generative Adversarial Nets (GANs), which have been
generating huge research interest in image generation and transformation.
Recently, researchers have attempted to apply GANs to missing data generation
and imputation for EHR data: a major challenge here is the categorical nature
of the data. State-of-the-art solutions to the GAN-based generation of
categorical data involve either reinforcement learning, or learning a
bidirectional mapping between the categorical and the real latent feature
space, so that the GANs only need to generate real-valued features. However,
these methods are designed to generate complete feature vectors instead of
imputing only the subsets of missing features. In this paper we propose a
simple and yet effective approach that is based on previous work on GANs for
data imputation. We first motivate our solution by discussing the reason why
adversarial training often fails in case of categorical features. Then we
derive a novel way to re-code the categorical features to stabilize the
adversarial training. Based on experiments on two real-world EHR data with
multiple settings, we show that our imputation approach largely improves the
prediction accuracy, compared to more traditional data imputation approaches.
