概述2022 n2c2共享任务在临床笔记中的上下文化药物事件提取
Diwakar Mahajan1, Jennifer J Liang1, Ching-Huei Tsou1
1IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America.
Journal of biomedical informatics
|June 25, 2023
概括
准确的药物历史需要了解临床笔记中的药物变化. 虽然NLP在提取方面表现出色,但对这些事件进行上下文化仍然是临床应用的挑战.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 临床信息学 临床信息学
- 生物医学数据科学 生物医学数据科学
背景情况:
- 准确的药物历史对于高质量的医疗保健至关重要.
- 了解临床笔记中的药物变化事件是必不可少的,但具有挑战性.
- 在没有临床背景的情况下提取药物变化对于实际使用是不够的.
研究的目的:
- 评估NLP系统,从临床笔记中提取药物变化事件及其背景.
- 推进对围绕药物事件的上下文信息的理解.
- 支持开发现实世界的临床应用.
主要方法:
- 2022年全国NLP临床挑战第一轨专注于上下文化药物事件.
- 定义了三个子任务:命名实体识别 (NER),事件检测和上下文提取 (动作,否定,时间性,确定性,演员).
- 基于变压器的大型语言模型主要由32个参与团队使用.
主要成果:
- 在NER系统中观察到高性能.
- 事件和上下文提取的性能明显较低.
- 挑战包括间接声明事件,依赖遥远的文本线索,以及每次提及的多个事件.
结论:
- 用于药物提取的NLP已经成熟,但上下文理解是一个开放的研究问题.
- 需要进一步的研究来弥合NLP能力和临床应用要求之间的差距.
- 改进语境提取是支持临床实践中准确服用药物史的关键.
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