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中国字符结构和边界特征的交叉相互作用,以改善临床命名实体识别

Ye Wang, Qi Wei, Hong Yu

IEEE journal of biomedical and health informatics

|August 4, 2025

概括

一个新的模型,中国字符结构和边界特征 (CCS) 的交叉交互,通过更好地识别令人困惑的医疗术语和符号,提高了临床命名实体识别 (CNER).

科学领域:

自然语言处理自然语言处理.
医疗信息学医疗信息学
计算语言学计算语言学

背景情况:

临床命名实体识别 (CNER) 对于处理医疗文本至关重要.
传统的CNER模型与新的临床术语和相似的实体作斗争.
区分"肺结节"和"肺结核"等实体是具有挑战性的.

研究的目的:

开发一种新的CNER模型,以解决识别未知的临床术语和混实体的局限性.
提高医疗实体在中国临床文本中的准确性和边界检测.

主要方法:

提出了一个名为"中文字结构和边界特征 (CCS) 的交叉交互"的新型NER模型.
杆中国字符结构特征和界限信息用于实体识别.
设计了一个交叉注意力机制,以捕捉实体间和角色激进的依赖关系.

主要成果:

与最先进的模型相比,CCS模型表现出更高的性能.
在多个公共医疗数据集上取得了重大改进,包括CCKS2020,CMeEE,CMI和IMCS.
增强语义理解和专业医疗术语的边界识别能力.

结论:

CCS模型有效地应对CNER的挑战,包括新词和语义上相似的实体.
拟议的方法在临床命名实体识别准确性方面取得了重大进展.
CCS提供了一个强大的解决方案,可以从临床叙述中提取结构化信息.

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