具有混合注意力和特征的EHR编码在疾病知识图表上传播.
Tianhan Xu1, Bin Li1, Ling Chen1
1School of Information Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou, 225127, Jiangsu, China.
Artificial intelligence in medicine
|June 23, 2024
概括
本研究介绍了KGENet,这是一种新型模型,通过利用疾病知识图表来增强电子健康记录 (EHR) 编码. KGENet提高了国际疾病分类 (ICD) 代码分配的准确性和可解释性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 电子健康记录 (EHR) 编码对于医疗应用至关重要,但由于长文本,大标签空间和不平衡的分布,它面临着挑战.
- 现有的方法忽视了国际疾病分类 (ICD) 代码中固有的疾病属性和关系.
研究的目的:
- 提出一个端到端的模型,KGENet,通过整合知识图来增强EHR编码.
- 通过结合多视角疾病属性和关系来解决先前研究的局限性.
主要方法:
- 构建了一个疾病知识图,详细介绍了ICD代码的多视图属性和关系.
- 采用长序编码器用于EHR文档表示.
- 利用混合注意力和图形传播,在KGENet.内增强知识.
主要成果:
- 与最先进的模型相比,KGENet在MIMIC-III基准数据集上表现优异.
- 该模型在预测效率和可解释性方面取得了改进.
- 根据疾病知识图表,生成了属性意识和关系增强的解释.
结论:
- KGENet有效地利用疾病知识图表来增强EHR编码.
- 拟议的模型提供了更好的准确性,并为预测提供了有价值的可解释性.
- 这种方法代表了EHR自动编码和医疗信息学方面的重大进步.
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