通过整合知识图和ClinicalBERT,对中国电子医疗记录进行命名实体识别
Xiang Xu1, Zhengxiong Li1, Hongwei Zhang1
1School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China.
Frontiers in artificial intelligence
|September 26, 2025
概括
本研究引入了在中国电子医疗记录 (EMR) 中命名实体识别 (NER) 的新方法. 通过结合ClinicalBERT,医学知识图表和字符特征,它显著提高了提取医学术语的准确性.
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:
- 一般语言模型在中国EMR中难以识别医疗术语.
- 在中国EMR中缺乏明确的词界限和复杂的表达式,这给NER带来了挑战.
研究的目的:
- 为中国EMR开发一种新的NER方法.
- 为了提高识别特定领域医疗术语的准确性.
主要方法:
- 将ClinicalBERT与使用Translating Embeddings (TransE) 的医学知识图集集成.
- 多个字符级特征的融合 (位置标签,上下文线索,语义嵌入).
- 使用BIOES标签,临床BERT编码,BiLSTM和CRF进行标签预测.
主要成果:
- 在公开可用的数据集上获得了89.44%的F1得分.
- 在NER性能方面表现优于现有的基线模型.
结论:
- 拟议的方法显著提高了中国EMR中的NER准确性.
- 集成特定领域的语言模型,结构化知识和性格特征是有效的.
- 显示了实际临床信息提取系统的潜力.
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