论文标题
对电子健康记录的生成对抗网络的评论：应用，评估措施和数据源
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
论文作者
论文摘要
电子健康记录（EHRS）是促进临床研究和护理应用的宝贵资产；但是，诸如数据隐私等许多挑战都涉及其最佳利用。深层生成模型，尤其是生成的对抗网络（GAN），通过学习基础数据分布的同时，在实现出色的性能和解决这些挑战方面通过学习基础数据分布来生成合成的EHR数据。这项工作旨在审查GAN在EHR的各种应用中的主要发展，并概述了拟议的方法论。为此，我们将医疗保健应用程序和机器学习技术的观点结合在一起，从源数据集以及生成的合成数据集的忠诚度和隐私评估中。我们还编译了审查作品使用的指标和数据集列表，这些指标和数据集可用于该领域的未来研究。我们结论是通过讨论EHRS开发的GAN中的挑战，并提出了推荐的做法。我们希望这项工作激发了医疗保健与机器学习交集的新研究开发方向。
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.
