临床文本中的不连续的命名实体:一个系统的文献综述
Areej Alhassan1, Viktor Schlegel2, Monira Aloud3
1University of Manchester, United Kingdom; King Saud University, Saudi Arabia.
Journal of biomedical informatics
|January 25, 2025
概括
在临床文本中不连续命名实体识别 (DNCR) 面临挑战. 本文重点介绍了DNCR的方法,强调了适应性解决方案的必要性,并探索了用于未来进步的生成语言模型.
科学领域:
- 计算语言学计算语言学
- 临床信息学是一种临床信息学.
- 自然语言处理自然语言处理.
背景情况:
- 从临床文本中提取命名实体是具有挑战性的,因为不连续的提及.
- 传统的命名实体识别 (NER) 方法与这些复杂的实体作斗争.
- 为了准确的临床NER,需要专门的计算解决方案.
研究的目的:
- 系统地审查临床文本中不连续命名实体识别 (DNCR) 的方法.
- 突出现有的DNCR方法的有效性和挑战.
- 确定临床DNCR的未来研究方向.
主要方法:
- 对不连续的命名实体进行了系统的文献审查.
- 搜索了四个计算机科学和一个医疗电子数据库.
- 分析了44项跨关键维度的研究,包括方法,体和性能.
主要成果:
- 确定了DNCR.方法的时间发展.
- 编目使用的公司和下游任务受不连续实体的影响.
- 报告了业绩成果,并讨论了遇到的挑战.
结论:
- 在DNCR方面取得的进展是显著的,但需要适应性,可通用的解决方案.
- 生成式语言模型为推进DNCR提供了一个有希望的方向.
- 未来的研究应该评估DNCR精度对临床任务的影响.
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