基于变压器的积极学习,用于多类文本注释和分类
Muhammad Afzal1, Jamil Hussain2, Asim Abbas3,4
1College of Computing, Birmingham City University, Birmingham, UK.
Digital health
|October 21, 2024
概括
这项研究引入了一个深度主动学习框架,用于临床笔记的自动注释,提高文本分类的准确性. 这种方法增强了医疗保健数据分析和临床文档实践.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 数据驱动的医疗保健依赖于标记的数据,但手动注释非结构化的临床笔记是具有挑战性的.
- 医疗数据中缺乏明确的标签,阻碍了有效的决策和分析.
研究的目的:
- 开发一种新的深度主动学习框架,以高效地对临床笔记进行多类文本分类.
- 使用SOAP (主观,目标,评估,计划) 框架自动化注释过程.
主要方法:
- 利用基于变压器的深度学习模型来自动注释临床笔记.
- 实施了深度主动学习框架,以促进注释过程.
主要成果:
- 在一套多样化的超过426份临床笔记上取得了卓越的分类准确性.
- 与现有方法相比,F1得分得到了4.8%的改善.
- 验证了医疗保健专业人员和临床文档的实际实用性.
结论:
- 积极学习和深度学习之间的协同作用推动了临床信息学中的自动文本注释.
- 未来的工作将探索多式联络数据和大型语言模型,以加强临床文本分析.
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