从自由文本药品标签到结构化药物术语,使用BERT和GPT
1The Australian E-Health Research Centre, CSIRO, Australia.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
我们开发了一种方法,使用命名实体识别 (NER) 和知识图从自由文本药物标签中提取药物细节. 这种方法增强了医疗保健系统中的结构化药物信息检索和管理.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 药理学 药理学是指药理学的学科.
背景情况:
- 控制药物术语缺乏在自由文本药物标签上找到的全面数据.
- 从非结构化文本有效地结构化药物信息对于医疗保健至关重要.
研究的目的:
- 开发和评估一种方法,以使用自由文本药物标签来丰富受控药物术语.
- 确定最有效的命名实体识别 (NER) 模型来提取药物实体.
主要方法:
- 对药物实体提取的各种NER模型 (基于规则的,深度学习,变压器,GPT-3,ChatGPT)进行了比较.
- 使用基于规则的关系提取算法来构建药物知识图.
- 开发了一种药物搜索方法,将提取的信息与术语服务器匹配起来.
主要成果:
- 作为一个NER模型,BERT-CRF表现出了最高的效率,达到95%的F度.
- 在术语规范化后,药物搜索方法在将标签与相关药物匹配时达到77%的准确性.
- 开发的模型可以作为网络服务部署,用于结构化药物信息检索.
结论:
- 拟议的方法有效地从自由文本来源中丰富受控药物术语.
- 这种方法可以改善不同卫生系统中药物信息的管理和可访问性.
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