Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning.

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

Abstract

OBJECTIVE: Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction.

Authors

  • Long Chen
    Department of Critical Care Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Yu Gu
    Microsoft Research, Redmond, WA, USA.
  • Xin Ji
    Beijing Shanggong Medical Technology Co., Ltd., China.
  • 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.
  • 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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