梳理电子病历事件流建模体系，统一定义与分类标准。
The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records
- 按事件时间、类型、数值处理方式建立新分类体系
- 系统总结监督与自监督训练策略，覆盖多种临床场景
- 适合医疗AI研究者快速掌握该领域脉络
电子病历（EHR）的广泛应用带来了涵盖检验、生命体征、用药和操作等异构临床数据，为人工智能在医疗领域的应用提供了巨大潜力。尽管传统建模方法多基于多变量时间序列，但难以应对真实临床流程中的稀疏性和不规则性。因此，研究逐渐转向事件流表示，将患者记录视为连续序列，以保留患者病程的精确时间结构。然而，现有文献仍零散割裂，存在定义不一致、建模范式多样、训练协议各异等问题。为此，本综述建立了EHR事件流的统一定义，并提出一种新型分类体系，依据模型对事件时间、类型和值的处理方式进行分类。系统回顾了从监督学习到自监督方法的训练策略，全面讨论了在各类临床场景中的应用。最后，识别出关键挑战与未来方向，旨在厘清当前研究格局，指导下一代医疗模型的发展。
原文摘要 · Abstract (English)
The widespread adoption of electronic health records (EHRs) enables the acquisition of heterogeneous clinical data, spanning lab tests, vital signs, medications, and procedures, which offer transformative potential for artificial intelligence in healthcare. Although traditional modelling approaches have typically relied on multivariate time series, they often struggle to accommodate the inherent sparsity and irregularity of real-world clinical workflows. Consequently, research has shifted toward event stream representation, which treats patient records as continuous sequences, thereby preserving the precise temporal structure of the patient journey. However, the existing literature remains fragmented, characterised by inconsistent definitions, disparate modelling architectures, and varying training protocols. To address these gaps, this review establishes a unified definition of EHR event streams and introduces a novel taxonomy that categorises models based on their handling of event time, type, and value. We systematically review training strategies, ranging from supervised learning to self-supervised methods, and provide a comprehensive discussion of applications across clinical scenarios. Finally, we identify open critical challenges and future directions, with the aim of clarifying the current landscape and guiding the development of next-generation healthcare models.
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