Title: Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories,
Abstract: Generative Adversarial Networks (GANs) represent a promising class of
generative networks that combine neural networks with game theory. From
generating realistic images and videos to assisting musical creation, GANs are
transforming many fields of arts and sciences. However, their application to
healthcare has not been fully realized, more specifically in generating
electronic health records (EHR) data. In this paper, we propose a framework for
exploring the value of GANs in the context of continuous laboratory time series
data. We devise an unsupervised evaluation method that measures the predictive
power of synthetic laboratory test time series. Further, we show that when it
comes to predicting the impact of drug exposure on laboratory test data,
incorporating representation learning of the training cohorts prior to training
GAN models is beneficial.