Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures

Details

• Alternative Title:
  J Biomed Inform
• Personal Author:
  Chen, Aokun ; Yu, Zehao ; Yang, Xi ; Guo, Yi ; Bian, Jiang ; Wu, Yonghui
• Description:
  Objective ; To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. ; Materials and methods ; We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using >90 billion words of text (including >80 billion words from >290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers. ; Results ; Our GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models. ; Conclusion ; This study demonstrated the advantage of using large transformer models for contextual medication information extraction from clinical narratives.
• Subjects:
  • Deep Learning
  • Natural Language Processing
  • Information Storage and Retrieval
• Keywords:
  • Medication information extraction
  • Named entity recognition
  • Text classification
  • Deep learning
  • Clinical natural language processing
• Source:
  J Biomed Inform. 142:104370
• Pubmed ID:
  37100106
• Pubmed Central ID:
  PMC10980542
• Document Type:
  Journal Article
• Funding:
  R56 AG069880/AG/NIA NIH HHSUnited States/ ; U18 DP006512/DP/NCCDPHP CDC HHSUnited States/ ; R01 MH121907/MH/NIMH NIH HHSUnited States/ ; R01 CA246418/CA/NCI NIH HHSUnited States/ ; U18DP006512/ACL/ACL HHSUnited States/ ; R21 CA253394/CA/NCI NIH HHSUnited States/ ; R21 CA245858/CA/NCI NIH HHSUnited States/ ; R01 DA050676/DA/NIDA NIH HHSUnited States/ ; R21 AG068717/AG/NIA NIH HHSUnited States/
• Volume:
  142
• Download URL:
  https://stacks.cdc.gov/view/cdc/153621/cdc_153621_DS1.pdf
• File Type:
  [PDF - 102.77 KB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:25c2e4d7e342d5a981efed5fe79128208908ce44f7ad354e79ab04e875e10360