The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records

2026-03-14

AI Summary

Electronic health record (EHR) data are inherently sparse and irregular, posing significant challenges for conventional multivariate time series modeling approaches, while existing event stream research lacks a unified framework. This work proposes to formalize EHRs as event streams and, for the first time, establishes a cohesive definition encompassing event timestamps, types, and associated values. Building upon this foundation, the study develops a systematic taxonomy of modeling approaches and integrates self-supervised, supervised, and temporal modeling paradigms to clarify training strategies in relation to clinical application scenarios. By synthesizing the current state of research, the paper identifies key challenges and outlines promising future directions, thereby providing both a theoretical foundation and practical guidance for the next generation of healthcare AI systems.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• event stream
• electronic health records
• taxonomy
• training protocols
• clinical data modelling

Innovation

• event stream modelling
• taxonomy
• electronic health records
• self-supervised learning
• temporal structure