This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of DPMs in synthesising longitudinal EHRs that capture mixed-type variables, including numeric, binary, and categorical variables. To our knowledge, this represents the first use of DPMs for this purpose. We compared our DPM-simulated datasets to previous state-of-the-art results based on generative adversarial networks (GANs) for two clinical applications: acute hypotension and human immunodeficiency virus (ART for HIV). Given the lack of similar previous studies in DPMs, a core component of our work involves exploring the advantages and caveats of employing DPMs across a wide range of aspects. In addition to assessing the realism of the synthetic datasets, we also trained reinforcement learning (RL) agents on the synthetic data to evaluate their utility for supporting the development of downstream machine learning models. Finally, we estimated that our DPM-simulated datasets are secure and posed a low patient exposure risk for public access.

翻译：本文提出了一种利用扩散概率模型（DPMs）模拟电子健康记录（EHRs）的新方法。具体而言，我们论证了DPMs在合成包含数值型、二值型及分类变量等混合类型变量的纵向EHRs方面的有效性。据我们所知，这是首次将DPMs用于此目的。我们将在两个临床应用中（急性低血压和人类免疫缺陷病毒的抗逆转录病毒治疗，ART for HIV）将DPM模拟数据集与先前基于生成对抗网络（GANs）的最优结果进行比较。鉴于此前缺乏类似的DPMs研究，本工作的核心内容在于从多维度探究应用DPMs的优势与注意事项。除了评估合成数据集的真实性，我们还在合成数据上训练强化学习（RL）智能体，以评估其对下游机器学习模型开发的支持效能。最后，我们评估了DPM模拟数据集的安全性，认为其面向公众开放时患者暴露风险较低。