Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Oct 1, 2020

Abstract

OBJECTIVE: Normalizing clinical mentions to concepts in standardized medical terminologies, in general, is challenging due to the complexity and variety of the terms in narrative medical records. In this article, we introduce our work on a clinical natural language processing (NLP) system to automatically normalize clinical mentions to concept unique identifier in the Unified Medical Language System. This work was part of the 2019 n2c2 (National NLP Clinical Challenges) Shared-Task and Workshop on Clinical Concept Normalization.

Authors

  • Long Chen
    Department of Critical Care Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Wenbo Fu
    Med Data Quest, San Diego, California, USA.
  • Yu Gu
    Microsoft Research, Redmond, WA, USA.
  • Zhiyong Sun
    Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.
  • Haodan Li
    Med Data Quest, Inc, La Jolla, California, USA.
  • Enyu Li
    Med Data Quest, San Diego, California, USA.
  • Li Jiang
    School of Food Science and Engineering, Hefei University of Technology, Hefei, China.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Yang Huang
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.

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