Yan Chao, Zhang Ziqi, Nyemba Steve, Li Zhuohang
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
JMIR AI. 2024 Apr 22;3:e52615. doi: 10.2196/52615.
Synthetic electronic health record (EHR) data generation has been increasingly recognized as an important solution to expand the accessibility and maximize the value of private health data on a large scale. Recent advances in machine learning have facilitated more accurate modeling for complex and high-dimensional data, thereby greatly enhancing the data quality of synthetic EHR data. Among various approaches, generative adversarial networks (GANs) have become the main technical path in the literature due to their ability to capture the statistical characteristics of real data. However, there is a scarcity of detailed guidance within the domain regarding the development procedures of synthetic EHR data. The objective of this tutorial is to present a transparent and reproducible process for generating structured synthetic EHR data using a publicly accessible EHR data set as an example. We cover the topics of GAN architecture, EHR data types and representation, data preprocessing, GAN training, synthetic data generation and postprocessing, and data quality evaluation. We conclude this tutorial by discussing multiple important issues and future opportunities in this domain. The source code of the entire process has been made publicly available.
合成电子健康记录（EHR）数据生成日益被视为一种重要的解决方案，可大规模扩展私人健康数据的可访问性并最大化其价值。机器学习的最新进展促进了对复杂高维数据更精确的建模，从而极大提高了合成EHR数据的数据质量。在各种方法中，生成对抗网络（GAN）因其能够捕捉真实数据的统计特征而成为文献中的主要技术路径。然而，该领域内关于合成EHR数据开发程序的详细指导却很匮乏。本教程的目的是以一个可公开获取的EHR数据集为例，展示一个生成结构化合成EHR数据的透明且可重复的过程。我们涵盖了GAN架构、EHR数据类型与表示、数据预处理、GAN训练、合成数据生成与后处理以及数据质量评估等主题。我们通过讨论该领域的多个重要问题和未来机遇来结束本教程。整个过程的源代码已公开提供。
