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.

arXiv commentNIPS ML4H 2017

References in corpus (6)

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• Adversarially Learned Inference
• Deep multi-scale video prediction beyond mean square error
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• MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
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Cited by in corpus (8)

• Synthesizing Tabular Data using Generative Adversarial Networks
• Modeling Tabular data using Conditional GAN
• A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
• CTAB-GAN: Effective Table Data Synthesizing
• Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
• Time-series Imputation and Prediction with Bi-Directional Generative Adversarial Networks
• Semantic Preserving Generative Adversarial Models
• Generation of Synthetic Electronic Medical Record Text