  Machine learning (ML) and Natural Language Processing (NLP) have achieved
remarkable success in many fields and have brought new opportunities and high
expectation in the analyses of medical data. The most common type of medical
data is the massive free-text electronic medical records (EMR). It is widely
regarded that mining such massive data can bring up important information for
improving medical practices as well as for possible new discoveries on complex
diseases. However, the free EMR texts are lacking consistent standards, rich of
private information, and limited in availability. Also, as they are accumulated
from everyday practices, it is often hard to have a balanced number of samples
for the types of diseases under study. These problems hinder the development of
ML and NLP methods for EMR data analysis. To tackle these problems, we
developed a model to generate synthetic text of EMRs called Medical Text
Generative Adversarial Network or mtGAN. It is based on the GAN framework and
is trained by the REINFORCE algorithm. It takes disease features as inputs and
generates synthetic texts as EMRs for the corresponding diseases. We evaluate
the model from micro-level, macro-level and application-level on a Chinese EMR
text dataset. The results show that the method has a good capacity to fit real
data and can generate realistic and diverse EMR samples. This provides a novel
way to avoid potential leakage of patient privacy while still supply sufficient
well-controlled cohort data for developing downstream ML and NLP methods. It
can also be used as a data augmentation method to assist studies based on real
EMR data.
