Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.

振荡器输出通常具有相位噪声，导致输出功率谱密度（PSD）分散在DIRAC DELTA功能周围。在本文中，考虑了AWGN信道，其中伴随相位噪声的发送信号被添加到信道高斯噪声并在接收器处接收。诸如最小均方（LMS）和平均MSE标准的传统信道估计算法不适用于该信道估计。我们（i）通过信息理论学习（ITL）标准，即最大正控性标准（MCC）分析该相位噪声信道估计，导致信道估计器稳定状态行为中的稳健性;（ii）通过将MSE和MCC组合为新的混合LMS算法来提高收敛速度。