A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models

TL;DR

This survey reviews recent AI advancements in electronic health record modeling, emphasizing deep learning and large language models, and discusses challenges, trends, and future directions for clinical applications.

Contribution

It provides a unified taxonomy of EHR modeling approaches, integrating deep learning, LLMs, and multimodal strategies, and highlights emerging trends and open challenges.

Findings

Introduction of a comprehensive taxonomy for EHR modeling

Review of recent deep learning and LLM-based methods

Discussion of future trends and open challenges in the field

Abstract

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/.