Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories
This work addresses a domain-specific problem in healthcare for predicting drug effects on lab tests, but it is incremental as it applies existing GAN methods to new EHR data.
The paper tackles the problem of predicting drug-induced laboratory test trajectories from electronic health records by proposing a framework using Generative Adversarial Networks (GANs) with representation learning, showing that this approach improves predictive power for synthetic time series data.
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.