CEHR-GPT：利用患者时间线生成电子健康记录
CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
AI总结：
本文提出CEHR-GPT方法，利用CEHR-BERT衍生的患者表示训练GPT模型，生成可转换为OMOP格式且保留时间依赖性的合成电子健康记录。
摘要：
合成电子健康记录（EHR）已成为推进医疗应用和机器学习模型的关键工具，尤其对于无法直接获取医疗数据的研究人员而言。尽管现有方法（如基于规则的方法和生成对抗网络（GANs））能够生成类似真实世界EHR数据的合成数据，但这些方法通常采用表格格式，忽略了患者病史中的时间依赖性，并限制了数据复制。近年来，利用生成式预训练Transformer（GPT）处理EHR数据的兴趣日益增长，这使得疾病进展分析、人群估计、反事实推理和合成数据生成等应用成为可能。在本工作中，我们聚焦于合成数据生成，并展示了使用一种源自CEHR-BERT的特定患者表示来训练GPT模型的能力，使我们能够生成可无缝转换为观察性医疗结果合作伙伴关系（OMOP）数据格式的患者序列。
英文摘要：
Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data. Although existing methods, like rule-based approaches and generative adversarial networks (GANs), generate synthetic data that resembles real-world EHR data, these methods often use a tabular format, disregarding temporal dependencies in patient histories and limiting data replication. Recently, there has been a growing interest in leveraging Generative Pre-trained Transformers (GPT) for EHR data. This enables applications like disease progression analysis, population estimation, counterfactual reasoning, and synthetic data generation. In this work, we focus on synthetic data generation and demonstrate the capability of training a GPT model using a particular patient representation derived from CEHR-BERT, enabling us to generate patient sequences that can be seamlessly converted to the Observational Medical Outcomes Partnership (OMOP) data format.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
