  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.
