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Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning

  • Feifan LiuEmail author
  • Chunhua Weng
  • Hong Yu
Chapter
Part of the Health Informatics book series (HI)

Abstract

Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and explains how traditional machine learning and the state-of-the-art deep learning techniques can be employed in the context of extracting and transforming narrative information in EHR to support clinical research.

Keywords

Electronic health records Biomedical natural language processing Rule-based approach Machine learning Deep learning Clinical research 

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Copyright information

© Springer International Publishing 2019

Authors and Affiliations

  1. 1.Department of Quantitative Health Sciences and Department of Radiology (Joint)University of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Department of Biomedical InformaticsColumbia UniversityNew YorkUSA
  3. 3.Department of Computer ScienceUniversity of Massachusetts LowellLowellUSA
  4. 4.Bedford VA Medical CenterBedfordUSA
  5. 5.Department of MedicineUniversity of Massachusetts Medical School (Adjunct)WorcesterUSA
  6. 6.College of Information and Computer SciencesUniversity of Massachusetts Amherst (Adjunct)AmherstUSA

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