skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

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

Journal of the American Medical Informatics Association : JAMIA, 2020-01, Vol.27 (1), p.56-64 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com 2019 ;The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com. ;ISSN: 1527-974X ;ISSN: 1067-5027 ;EISSN: 1527-974X ;DOI: 10.1093/jamia/ocz141 ;PMID: 31591641

Full text available

Citations Cited by
  • Title:
    Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning
  • Author: Chen, Long ; Gu, Yu ; Ji, Xin ; Sun, Zhiyong ; Li, Haodan ; Gao, Yuan ; Huang, Yang
  • Subjects: Deep Learning ; Drug-Related Side Effects and Adverse Reactions ; Electronic Health Records ; Humans ; Information Storage and Retrieval - methods ; Knowledge Bases ; Narration ; Natural Language Processing ; Neural Networks, Computer ; Research and Applications ; Unified Medical Language System
  • Is Part Of: Journal of the American Medical Informatics Association : JAMIA, 2020-01, Vol.27 (1), p.56-64
  • Description: 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. Materials and Methods The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks. Results The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had <2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement. Conclusions We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions.
  • Publisher: England: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1527-974X
    ISSN: 1067-5027
    EISSN: 1527-974X
    DOI: 10.1093/jamia/ocz141
    PMID: 31591641
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    MEDLINE
    PubMed Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait