系统梳理AI建模电子病历的前沿方法与挑战
A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models
- 构建五维统一分类框架，覆盖数据、架构、学习策略等
- 涵盖从深度学习到大模型的主流方法，支持临床知识融合
- 适合研究医疗AI、临床决策支持的学者与开发者
人工智能在电子健康记录（EHR）分析与建模方面展现出巨大潜力，但其固有的异质性、时间不规则性和领域特异性，带来了与视觉和自然语言任务截然不同的挑战。本综述全面梳理了深度学习、大语言模型（LLMs）与EHR建模交叉领域的最新进展。提出一个包含五个关键设计维度的统一分类体系：以数据为中心的方法、神经架构设计、学习策略、多模态学习及基于大模型的建模系统。每个维度下综述代表性方法，涵盖数据质量提升、结构与时间表示、自监督学习以及临床知识整合。同时指出新兴趋势，如基础模型、大模型驱动的临床智能体及EHR到文本的转换以支持下游推理。最后讨论基准测试、可解释性、临床对齐性及跨临床场景泛化等开放挑战。旨在为推进AI驱动的EHR建模与临床决策支持提供系统性路线图。完整方法列表见 https://survey-on-tabular-data.github.io/。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the inherent heterogeneity, temporal irregularity, and domain-specific nature of EHR data present unique challenges that differ fundamentally from those in vision and natural language tasks. This survey offers a comprehensive overview of recent advancements at the intersection of deep learning, large language models (LLMs), and EHR modeling. We introduce a unified taxonomy that spans five key design dimensions: data-centric approaches, neural architecture design, learning-focused strategies, multimodal learning, and LLM-based modeling systems. Within each dimension, we review representative methods addressing data quality enhancement, structural and temporal representation, self-supervised learning, and integration with clinical knowledge. We further highlight emerging trends such as foundation models, LLM-driven clinical agents, and EHR-to-text translation for downstream reasoning. Finally, we discuss open challenges in benchmarking, explainability, clinical alignment, and generalization across diverse clinical settings. This survey aims to provide a structured roadmap for advancing AI-driven EHR modeling and clinical decision support. For a comprehensive list of EHR-related methods, kindly refer to https://survey-on-tabular-data.github.io/.
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