The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 32968800

Abstract

OBJECTIVE: The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task track 3, focused on medical concept normalization (MCN) in clinical records. This track aimed to assess the state of the art in identifying and matching salient medical concepts to a controlled vocabulary. In this paper, we describe the task, describe the data set used, compare the participating systems, present results, identify the strengths and limitations of the current state of the art, and identify directions for future research.

Authors

  • Sam Henry
    Department of Computer Science, Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA. Electronic address: henryst@vcu.edu.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Feichen Shen
    Department of Health Sciences Research, Rochester MN.
  • Ozlem Uzuner
    Department of Information Studies, University at Albany, SUNY. Albany, NY.

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