most citedGenerative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories

35 citations · 35 across the 1 of their papers we have counts for

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1 paper

cs.LG2017★ 35 cited

Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories

Alexandre Yahi, Rami Vanguri, Noémie Elhadad +1

Generative Adversarial Networks (GANs) represent a promising class of generative networks that combine neural networks with game theory. From generating realistic images and videos…