基于临床文本树结构的ICD代码映射模型
1School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
Artificial intelligence in medicine
|May 30, 2025
概括
通过深度学习,TRIC模型增强了临床记录的自动国际疾病分类 (ICD) 编码. 这种用于ICD编码 (TRIC) 的变压器和树木模型提高了医疗记录分类的准确性和效率.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 深度学习方法提高了电子医疗记录 (EMR) 编码效率,取代了手工流程.
- 在表示临床文本语义和结合结构记录特征方面仍然存在挑战.
- 现有的模型在处理非结构化临床数据的复杂性方面扎,以获得准确的ICD编码.
研究的目的:
- 为增强的自动ICD编码提出TRansformer和TRee-lstm用于ICD编码 (TRIC) 模型.
- 解决临床记录中语义表示和结构特征提取方面的局限性.
- 为了提高非结构化临床文本与ICD代码的映射的准确性和效率.
主要方法:
- TRIC模型整合了选区树和基于变压器的模型来进行特征提取.
- 树-lstm用于丰富临床记录特征.
- 生物BERT用于突出显示关键的ICD编码元素并改善匹配.
- 一个完全连接的神经网络分类器执行许多对许多映射.
主要成果:
- 与12个基准模型相比,TRIC模型在MIMIC-III数据集上取得了更好的表现.
- 关键的绩效指标包括MiF (0.586),MaF (0.109),MiAUC (0.989),MaAUC (0.937) 和P@8 (0.758) 等.
- 这些结果显示了自动ICD编码质量的显著改善.
结论:
- TRIC模型有效地解决了临床记录中语义表示和结构特征的挑战.
- 它为准确和高效的非结构化EMR数据的自动ICD编码提供了强大的解决方案.
- 该研究验证了TRIC模型提高ICD自动编码质量的能力.
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