# Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

arXiv: https://arxiv.org/abs/1706.03446 · HTML: https://mlsanity.com/paper/1706.03446/

## Key facts

- Cited by 148 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

## Built on by (most cited first, 30 of 148)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Medical Visual Question Answering: A Survey](https://mlsanity.com/paper/2111.10056/) (2021)
- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images](https://mlsanity.com/paper/2207.07027/) (2022)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling](https://mlsanity.com/paper/2210.12156/) (2022)
- [Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models](https://mlsanity.com/paper/2310.15290/) (2023)
- [Emergency Department Decision Support using Clinical Pseudo-notes](https://mlsanity.com/paper/2402.00160/) (2024)
- [NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports](https://mlsanity.com/paper/2305.03598/) (2023)
- [Zero-shot information extraction from radiological reports using ChatGPT](https://mlsanity.com/paper/2309.01398/) (2023)
- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models](https://mlsanity.com/paper/2306.05052/) (2023)
- [MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to 200K Tokens](https://mlsanity.com/paper/2406.15019/) (2024)
- [MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning](https://mlsanity.com/paper/2502.15954/) (2025)
- [Sequential Diagnosis Prediction with Transformer and Ontological Representation](https://mlsanity.com/paper/2109.03069/) (2021)
- [ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling](https://mlsanity.com/paper/2504.07373/) (2025)
- [Differentially Private Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2106.06127/) (2021)
- [Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2408.07705/) (2024)
- [RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements](https://mlsanity.com/paper/2411.15700/) (2024)
- [Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM](https://mlsanity.com/paper/2403.08818/) (2024)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)
- [Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health](https://mlsanity.com/paper/2108.10761/) (2021)
- [Large language models are good medical coders, if provided with tools](https://mlsanity.com/paper/2407.12849/) (2024)
- [TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation](https://mlsanity.com/paper/2504.17613/) (2025)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data](https://mlsanity.com/paper/2305.02993/) (2023)
- [A Method to Facilitate Membership Inference Attacks in Deep Learning Models](https://mlsanity.com/paper/2407.01919/) (2024)
- [Deep learning models are not robust against noise in clinical text](https://mlsanity.com/paper/2108.12242/) (2021)

## Abstract (arXiv)

The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR). While primarily designed for archiving patient clinical information and administrative healthcare tasks, many researchers have found secondary use of these records for various clinical informatics tasks. Over the same period, the machine learning community has seen widespread advances in deep learning techniques, which also have been successfully applied to the vast amount of EHR data. In this paper, we review these deep EHR systems, examining architectures, technical aspects, and clinical applications. We also identify shortcomings of current techniques and discuss avenues of future research for EHR-based deep learning.