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

• Published in: IEEE journal of biomedical and health informatics Vol. 22; no. 5; pp. 1589 - 1604
• Main Authors: Shickel, Benjamin, Tighe, Patrick James, Bihorac, Azra, Rashidi, Parisa
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Archives & records; Archiving; Benchmarks; Clinical informatics; Deep Learning; Electronic Health Records; Electronic medical records; Health care; Heterogeneity; Hospitals; Humans; Informatics; Information retrieval; Learning algorithms; Machine learning; Medical diagnosis; Medical diagnostic imaging; Phenotyping; survey; Therapeutic applications
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2017.2767063

The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has seen widespread advances in the field of deep learning. In this review, we survey the current research on applying deep learning to clinical tasks based on EHR data, where we find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification. We identify several limitations of current research involving topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. We conclude by summarizing the state of the field and identifying avenues of future deep EHR research.