Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
June 12, 2017 ยท Declared Dead ยท ๐ IEEE journal of biomedical and health informatics
"No code URL or promise found in abstract"
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Authors
Benjamin Shickel, Patrick Tighe, Azra Bihorac, Parisa Rashidi
arXiv ID
1706.03446
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
1.3K
Venue
IEEE journal of biomedical and health informatics
Last Checked
2 months ago
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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