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.

本文提出了一个框架，探索将生成对抗网络应用于连续实验室时间序列数据的价值，提出一种无监督的评估方法来测量合成实验室测试时间序列的预测能力，并证明在训练 GAN 模型之前，将训练集的表示学习纳入考虑是有益的，尤其是在预测药物作用于实验室测试数据方面。

基于生成对抗网络的电子健康记录研究：探索和评估药物诱导的实验室检测轨迹预测方法的框架

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