Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients’ independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder.

中文翻译：

---

使用双对抗自动编码器生成顺序电子健康记录

最近对电子健康记录 (EHR) 的研究开始学习深度生成模型并合成大量真实记录，以解决围绕 EHR 的重大隐私问题。然而，他们中的大多数只关注关于患者独立就诊的结构化记录，而不是按时间顺序排列的临床记录。在本文中，我们的目标是学习和合成基于生成自编码器的真实 EHR 序列。