中国医疗命名实体识别利用实体协会和门语境意识
Yang Yan1, Yufeng Kang1, Wenbo Huang1
1Institution of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China.
PloS one
|February 25, 2025
概括
这项研究引入了一种新的深度学习模型,用于增强中医命名实体识别. 该模型实现了卓越的性能,改善了信息提取,以便更好地做出临床决策.
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:
- 准确的医疗命名实体识别 (NER) 对医疗保健中的深度学习应用至关重要.
- 目前的NER方法与上下文意识和实体间关系作斗争.
- 从医学文本中有效地提取信息,对于改善医疗保健服务和临床决策至关重要.
研究的目的:
- 为中国医疗NER开发一个先进的深度学习模型.
- 解决现有的NER方法中的上下文理解和实体交互的局限性.
- 提高处理医学文献的准确性和效率.
主要方法:
- 利用中国预训练的RoBERTa-wwm-ext模型进行上下文特征提取.
- 采用具有多头注意力的循环神经网络,用于并行处理和捕获实体间的依赖关系.
- 集成条件随机场与交叉损失,以提高准确性和标签序列的一致性.
主要成果:
- 拟议的模型在MCSCSet数据集上获得了91.90%的F1得分.
- 该模型在CMeEE数据集上获得了64.36%的F1得分.
- 在中国医疗NER任务中表现优于现有的相关模型.
结论:
- 开发的模型在识别中国医学文本中的命名实体方面表现出显著的有效性.
- 该方法有效地捕捉了上下文信息和实体间的依赖关系.
- 这一进步有望增强医疗信息处理和临床支持系统.
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