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Sharing\nsynthetic EHR data could mitigate risk. In this paper, we propose a new\napproach, medical Generative Adversarial Network (medGAN), to generate\nrealistic synthetic patient records. Based on input real patient records,\nmedGAN can generate high-dimensional discrete variables (e.g., binary and count\nfeatures) via a combination of an autoencoder and generative adversarial\nnetworks. We also propose minibatch averaging to efficiently avoid mode\ncollapse, and increase the learning efficiency with batch normalization and\nshortcut connections. To demonstrate feasibility, we showed that medGAN\ngenerates synthetic patient records that achieve comparable performance to real\ndata on many experiments including distribution statistics, predictive modeling\ntasks and a medical expert review. 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