在临床笔记中检测药物提及和药物变化事件,使用基于变压器的模型
Yuting Guo1, Yao Ge1, Abeed Sarker1
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, United States.
Studies in health technology and informatics
|January 25, 2024
概括
本研究引入了先进的自然语言处理 (NLP) 模型,用于从临床文本中提取药物信息和分类事件,实现事件分类的0.926 F1得分.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
- 生物医学数据科学 生物医学数据科学
背景情况:
- 临床文本分析对于提取有价值的信息至关重要.
- 药物提取,事件分类和上下文分类是医疗保健中的关键NLP任务.
研究的目的:
- 从临床笔记开发和评估NLP模型,用于从临床笔记中提取药物和分类任务.
- 提高在电子健康记录中识别药物,事件及其背景的准确性.
主要方法:
- 使用BioClinicalBERT进行命名实体识别 (NER) 来识别药物提及.
- 开发了一个统一的模型架构,使用BioClinicalBERT和RoBERTa进行事件和上下文分类.
- 实施了基于字典的模糊匹配机制,用于药物识别.
- 应用了一种集合方法,将多个预训练模型的预测结合起来.
主要成果:
- 在事件分类中获得了0.926微平均F1分数,比基线高出5%.
- 在n2c2共享任务评估中,开发的系统始终处于前10名.
- 证明了有效的药物提取和分类能力.
结论:
- 拟议的NLP模型在分析临床文本以获取药物和事件信息方面显示出显著的改进.
- 组合方法结合了预先训练的模型,提高了分类性能.
- 这种方法对于现实世界的临床数据挑战是有效的.
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