语境化药物事件提取与步行NER和多回合QA.
Tomoki Tsujimura1, Koshi Yamada1, Ryuki Ida1
1Computational Intelligence Laboratory, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku-ku, Nagoya, 468-8511, Aichi, Japan.
Journal of biomedical informatics
|June 15, 2023
概括
这项研究引入了一种新的系统,用于从临床笔记中提取药物变化及其背景. 该方法在n2c2 2022药物事件提取挑战中获得了最高分.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 生物医学文本挖掘 生物医学文本挖掘
背景情况:
- 从临床笔记中自动提取药物事件对于患者安全和药物监测至关重要.
- 在临床文本分析中,确定药物变化及其相关背景 (例如,原因,时间) 仍然是一个挑战.
研究的目的:
- 开发和评估一种从临床笔记中抽取上下文化药物事件的系统.
- 准确识别药物名称跨度,分类药物更改事件,并确定它们的背景.
主要方法:
- 一个管道系统,将步步步步的命名实体识别 (NER) 模型与集成分类模型 (基于跨度和问答) 结合起来,用于事件和上下文分类.
- 步行式NER模型使用重叠次序和大型预训练语言模型处理临床文本.
- 采用多轮问答和基于跨度的模型进行事件和上下文分类.
主要成果:
- 该系统在n2c2 2022 Track 1数据集上实现了端到端上下文化药物事件提取的66.47%的F-score.
- 这一表现代表了n2c2 2022赛道1挑战的参与者中最高的得分.
- 该系统在药物提取 (ME),事件分类 (EC) 和上下文分类 (CC) 中表现出有效性.
结论:
- 开发的系统有效地从临床笔记中提取上下文化药物事件.
- 拟议的管道架构,将步行NER与整体分类集成在一起,对于临床文本分析非常有效.
- 这项工作推进了自动化从电子健康记录中提取药物事件的最先进技术.
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