  In recent years, deep learning has been successfully adopted in a wide range
of applications related to electronic health records (EHRs) such as
representation learning and clinical event prediction. However, due to privacy
constraints, limited access to EHR becomes a bottleneck for deep learning
research. To mitigate these concerns, generative adversarial networks (GANs)
have been successfully used for generating EHR data. However, there are still
challenges in high-quality EHR generation, including generating time-series EHR
data and imbalanced uncommon diseases. In this work, we propose a Multi-label
Time-series GAN (MTGAN) to generate EHR and simultaneously improve the quality
of uncommon disease generation. The generator of MTGAN uses a gated recurrent
unit (GRU) with a smooth conditional matrix to generate sequences and uncommon
diseases. The critic gives scores using Wasserstein distance to recognize real
samples from synthetic samples by considering both data and temporal features.
We also propose a training strategy to calculate temporal features for real
data and stabilize GAN training. Furthermore, we design multiple statistical
metrics and prediction tasks to evaluate the generated data. Experimental
results demonstrate the quality of the synthetic data and the effectiveness of
MTGAN in generating realistic sequential EHR data, especially for uncommon
diseases.
