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

TL;DR

This survey reviews recent deep learning methods applied to electronic health records, highlighting architectures, applications, challenges, and future research directions in clinical informatics.

Contribution

It provides a comprehensive overview of deep learning techniques in EHR analysis, emphasizing recent advances, technical insights, and identifying gaps for future exploration.

Findings

Deep learning architectures have been successfully applied to EHR data.

Current techniques face challenges like data heterogeneity and interpretability.

Future research should focus on addressing these limitations and improving clinical utility.

Abstract

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.

Full text

See pages 1-last of DeepEHR2.pdf