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

2017/06/30 by Benjamin Shickel, Patrick Tighe, Azra Bihorac +1 · 4 citations
Computer Science · Mathematics · #cs.LG #stat.ML

paper · pdf · doi:10.1109/jbhi.2017.2767063

Accepted for publication with Journal of Biomedical and Health Informatics: http://ieeexplore.ieee.org/abstract/document/8086133/

arxiv created 2018/02/24 · arxiv updated 2018/02/27

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.

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