Grouped Correlational Generative Adversarial Networks for Discrete Electronic Health Records Using Generative Adversarial Networks (GANs) to generate synthetic Electronic Health Records (EHR) has attracted increasing attention. However, in existing approaches, the events in EHRs are treated as separate variables which are indiscriminately entered into the model, without taking into account the meaning and grouping of them. Besides, the efficacy of treatment is often neglected. In this paper, we first embed the efficacy information into the disease diagnosis, and then propose Grouped Correlational GAN (GcGAN) to explicitly learn inherent correlations between different groups of variables. We also introduce a dense connection to strengthen the generator capacity in GcGAN. Experimental results on realworld data demonstrate that the generated data from GcGAN are able to simulate realworld data in terms of distribution statistics. The results on multilabel treatment recommendation tasks show that GcGAN can boost the performances by augmenting the training dataset with the generated data and outperforms stateoftheart approaches. It can also automatically distinguish between diseasespecific drugs and adjuvant drugs, which enhances the model interpretability.

Privacy Preserving Synthetic Health Data We examine the feasibility of using synthetic medical data generated by GANs in the classroom, to teach data science in health informatics. We present an endtoend methodology to retain instructional utility, while preserving privacy to a level, which meets regulatory requirements: (1) a GAN is trained by a certified medicaldata securityaware agent, inside a secure environment; (2) the final GAN model is used outside of the secure environment by external users (instructors or researchers) to generate synthetic data. This second step facilitates data handling for external users, by avoiding deidentification, which may require special user training, be costly, andor cause loss of data fidelity. We benchmark our proposed GAN versus various baseline methods using a novel set of metrics. At equal levels of privacy and utility, GANs provide small footprint models, meeting the desired specifications of our application domain. Data, code, and a challenge that we organized for educational purposes are available.