通过强大的图形神经网络打破ICD分类中的障碍,用于层次编码
Suyang Xi1, Jiesen Shi1, Jiachen Yan2
1School of Artificial Intelligence and Robotics, Xiamen University Malaysia, Sepang, Malaysia.
Scientific reports
|July 15, 2025
概括
本研究介绍了LGG-NRGrasp,这是一种用于国际疾病分类 (ICD) 代码的自动化分类的新型框架. 该方法通过将ICD编码建模为图表生成问题来提高临床文档的准确性和可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 改善临床文档 改善临床文档
背景情况:
- 准确的国际疾病分类 (ICD) 代码分类对于临床文档至关重要.
- 现有的自动化方法难以应对医疗文本的复杂性和细微差别.
- 传统模型在处理稀疏的医疗数据时缺乏灵活性和稳定性.
研究的目的:
- 提出一个先进的对抗式学习框架,LGG-NRGrasp,用于自动化ICD编码.
- 解决现有方法在灵活性,稳定性和处理复杂医疗文本方面的局限性.
- 为了提高诊断代码分配到医疗出院总结的准确性和可靠性.
主要方法:
- 开发了标记图形生成与节点表示掌握 (LGG-NRGrasp),一个对抗式学习框架.
- 模拟ICD编码作为标记图形生成问题,结合特征学习的层次结构.
- 集成的对抗性强化学习和域调整技术,以提高概括性.
主要成果:
- 与基准数据集上的领先模型相比,LGG-NRGrasp表现优越.
- 该框架有效地解决了深度图形神经网络中的过度平滑问题.
- 在自动化ICD代码分类中实现了更高的性能和可靠性.
结论:
- LGG-NRGrasp为自动化ICD编码提供了强大而灵活的解决方案.
- 拟议的方法显著提高了诊断代码分配的准确性.
- 这一框架推动了通过人工智能改善临床文档的领域.
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