Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs

  • Runjie ZhuEmail author
  • Xinhui TuEmail author
  • Jimmy HuangEmail author
Part of the Studies in Big Data book series (SBD, volume 68)


Natural language processing (NLP) is an interdisciplinary domain of research that focuses on the interactions between human languages and computers. There has been a recent trend of solving the NLP problems using deep learning approach. The applications of deep learning in the healthcare sector are mostly considered to be related to canonical examples of applying image processing and computer vision techniques to medical scans for disease diagnoses. Electronic Health Record (EHR) is another source of data often being neglected, equally if not more important than medical scans, that can change the way we learn useful features and information from the medical records of patients. These text-based information stored within the EHR are data-rich by nature, but are often not well-understood due to its characteristics of high volume, variety, velocity and complexity. However, these specific characteristics fit right to the nature of deep learning. Therefore, we believe it is the right time to summarize the current status, to review and learn from the state-of-the-art medical-based NLP techniques. Different from the existing reviews, we examine and categorize the current deep learning-based NLP techniques in medical domain into three major purposes: representation learning, information extraction and clinical predictions. Meanwhile, we discuss whether the application of deep learning methods has tackled the problems differently and transformed these tasks revolutionarily. Based on the results, we find that the distance to revolutionize the existing healthcare sector using deep learning methods still remains long. However, the recent progress made by these proposed methods have already made a promising good start. Furthermore, we state some of the legal and ethical considerations, present the status quo of the healthcare industry applications, and provide several possible directions of future research.


Deep learning Electronic health records Natural language processing Representation learning Information extraction Clinical predictions 



This work is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada, an NSERC CREATE award in ADERSIM,1 the York Research Chairs (YRC) program and an ORF-RE (Ontario Research Fund-Research Excellence) award in BRAIN Alliance.2


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Authors and Affiliations

  1. 1.Information Retrieval and Knowledge Management Research Lab, Department of Electrical Engineering and Computer ScienceYork UniversityTorontoCanada
  2. 2.School of Computer ScienceCentral China Normal UniversityWuhanChina
  3. 3.Information Retrieval and Knowledge Management Research Lab, School of Information TechnologyYork UniversityTorontoCanada

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