发表机构
Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SynEHR框架，通过两项创新模块优化纵向EHR合成，在真实数据集的多维度评估中优于现有最优模型，生成更具临床一致性与时间真实性的EHR数据。
AI 中文摘要
纵向电子健康记录（EHR）记录患者随时间推移的临床就诊序列，保留了疾病进展与诊疗过程的时间演化特征。然而，真实的纵向EHR因包含大量细粒度、患者特异性信息而难以获取，因此合成EHR生成成为保留患者就诊轨迹统计模式与临床结构的重要方法，可在真实记录有限时支持更广泛的建模与分析。尽管近期生成模型在生成未来就诊序列方面取得了进展，但仍存在局限：未明确整合EHR中就诊间不规则时间演化与就诊内临床事件结构，导致生成的就诊序列临床一致性差、时间上不真实。本研究提出SynEHR，一种轻量级、基于大语言模型（LLM）的纵向EHR合成框架，包含两项创新设计：一是时间状态条件模块，用于捕获跨就诊的不规则时间状态；二是时间关系适配模块，将这些状态与患者病史结合，动态构建患者特异性关系表示。SynEHR基于参数高效的LoRA适配语言模型生成器构建，具备下一次就诊生成能力，用于训练上述两个模块以实现时间与临床信息驱动的生成。在真实世界EHR数据集上开展的保真度、隐私性及下游效用评估的大量实验表明，SynEHR在生成更具临床一致性、时间真实性的纵向EHR数据方面，优于现有最优模型。
英文摘要
Longitudinal electronic health records (EHRs) document patients' sequences of clinical visits over time, preserving the temporal evolution of disease progression and care delivery. However, real longitudinal EHRs are difficult to access because they contain large amounts of fine-grained, patient-specific information. Synthetic EHR generation therefore provides a valuable approach for preserving the statistical patterns and clinical structure of patient visit trajectories, enabling broader modeling and analysis when real records are limited. Although recent generative models have made progress in producing future visit sequences, they remain limited in explicitly integrating inter-visit irregular temporal evolution and intra-visit clinical event structures in EHRs, leading to clinically inconsistent and temporally unrealistic visit sequences. In this work, we propose SynEHR, a lightweight adaptive LLM-based framework for longitudinal EHR synthesis. There are two novel designs in SynEHR, i.e., a Temporal State Conditioning Module captures irregular temporal states across visits and a Temporal-Relational Adaptation Module combines these states with patient history to dynamically construct patient-specific relational representations. SynEHR then builds on a parameter-efficient LoRA-adapted language-model generator with next-visit generation capability to train the two modules for temporally and clinically informed generation. Extensive experiments on real-world EHR datasets across fidelity, privacy, and downstream utility evaluations demonstrate that SynEHR outperforms state-of-the-art models by generating more clinically coherent and temporally faithful longitudinal EHR data.
Comments12 pages. CIKM '26
