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基于域名知识和位置编码的医疗命名实体识别

Shuifa Sun¹, Qin Hu¹, Fengjiao Xu²

¹School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.

BMC medical informatics and decision making

|July 2, 2025

概括

本研究介绍了一种名为实体识别的中国电子医疗记录的新型模型,通过整合域知识和先进的位置编码来提高准确性. 该模型增强了边界检测,以便更好地分析临床数据.

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
人工智能的人工智能

背景情况:

在中国电子医疗记录 (EMR) 中,准确的命名实体识别 (NER) 对临床数据分析至关重要.
现有的模型经常在精确的边界检测和纳入特定领域的医学术语方面扎.

研究的目的:

为中国EMR开发一种新的NER模型,增强边界检测能力.
利用医学领域的知识和先进的位置编码技术来提高识别精度.

主要方法:

一个BERT模块与医学领域特定术语的词汇适配器集成.
使用Star-Transformer用于本地特征和BiLSTM用于远程特征的特征提取.
整合旋转位置嵌入 (RoPE) 来增强语义特征提取.

主要成果:

在CCKS2020数据集上获得了85.78%的F1得分,比基线增加了2.96%.
在自制乳腺癌超声波报告数据集上表现出更好的性能.
验证了该模型在医学领域的有效性和适用性.

结论:

拟议的模型显著改善了在中国EMR中命名实体的识别.
域知识,高级编码和特定架构的整合增强了边界检测和整体准确性.
该模型显示了在处理临床文本数据方面的真实应用的巨大潜力.

关键词:

贝尔特 (BERT) 公司医学领域字典医学领域字典命名实体认可命名实体认可这就是为什么RoPE是RoPE.星星变压器是什么意思

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