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.

翻译：目的：开发一个自然语言处理（NLP）系统，用于提取药物及其上下文信息，以帮助理解药物变化。本项目是2022年n2c2竞赛的一部分。材料与方法：我们开发了用于药物提及提取、事件分类（指示是否讨论了药物变化）以及上下文分类的NLP系统，其中上下文分类将药物变化情境划分为与药物变化相关的5个正交维度。针对这三个子任务，我们探索了6种最先进的预训练Transformer模型，包括GatorTron——一种基于超过900亿词文本（其中包含从佛罗里达大学健康中心识别的超过2.9亿份临床记录中提取的超过800亿词）预训练的大型语言模型。我们使用2022年n2c2组织者提供的标注数据和评估脚本对NLP系统进行了评估。结果：我们的GatorTron模型在药物提取任务上取得了最佳F1分数0.9828（排名第三），事件分类任务上为0.9379（排名第二），上下文分类任务上取得了最佳微观平均准确率0.9126。GatorTron优于使用较小规模通用英语文本和临床文本语料库预训练的现有Transformer模型，显示了大型语言模型的优势。结论：本研究证明了使用大型Transformer模型从临床叙述中提取上下文化药物信息的优势。