从临床注释中提取不良药物事件:使用的方法的系统审查
Salisu Modi1, Khairul Azhar Kasmiran2, Nurfadhlina Mohd Sharef2
1Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia; Department of Computer Science, Sokoto State University, Sokoto, Nigeria.
Journal of biomedical informatics
|February 8, 2024
概括
本综述总结了从临床笔记中提取不良药物事件 (ADEs) 的方法,这对于药监和药物研究至关重要. 它强调了自然语言处理 (NLP) 方法和数据集的进步,指导了未来的研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床自然语言处理 临床自然语言处理
- 药物监督研究 药物监督研究
背景情况:
- 药物不良事件 (ADEs) 是药物的有害影响.
- 从临床笔记中提取ADEs对于患者安全和药物研究至关重要.
- 生物医学文本提取研究的重点是识别ADEs及其属性.
研究的目的:
- 从非结构化临床笔记中抽取药物不良反应 (ADEs) 的现有方法进行系统审查.
- 提供用于ADE提取的自然语言处理 (NLP) 技术的概述.
- 确定ADE提取研究的趋势,挑战和未来方向.
主要方法:
- 在主要的生物医学和多学科数据库 (PubMed,Medline,IEEE Xplore,Scopus,ScienceDirect,ACL Anthology) 中从2022年6月到2023年12月进行了系统的文献搜索.
- 遵循PRISMA 2020系统审查指南,最初确定了5537篇文章,最后分析了82篇出版物.
- 基于提取任务 (NER,RE,联合,管道,多任务) 和数据提取方法 (基于规则的,ML,DL,混合,LLM) 的分类研究.
主要成果:
- 命名实体识别 (NER) 和关系提取 (RE) 是ADE提取的关键任务.
- 方法包括管道,联合任务学习和多任务学习,大量研究侧重于单个任务 (NER: 27, RE: 20).
- 深度学习方法主导ADE提取 (32项研究),其次是混合方法 (12) 和机器学习 (11),其中MADE 1.0,TAC 2017和n2c2 2018是常见的数据集.
结论:
- 从临床文本中提取ADEs对于药监和药物安全至关重要.
- 本综述整合了ADE提取当前的进展,方法和数据集.
- 确定了挑战和未来的研究途径,以鼓励该领域的创新.
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