通过内在标签增强医疗知识图的错误检测
Guangya Yu1,2, Qi Ye2, Tong Ruan2
1Zhejiang Laboratory, Hangzhou 311121, China.
Bioengineering (Basel, Switzerland)
|March 27, 2024
概括
本研究介绍了EMKGEL,这是一种通过使用内在标签信息来增强医疗知识图 (MKG) 的错误检测的新方法. 埃姆克吉尔提高了MKG的准确性,这对于可靠的医疗保健应用至关重要.
科学领域:
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 医疗知识图 (MKG) 越来越多地使用自动化方法构建,引入可能降低医疗保健应用性能的噪音.
- 目前的错误检测方法依赖于图形拓和外部实体标签,这些标签的准确获取是昂贵的.
- 在MKG中缺乏精确的实体标签,这阻碍了其质量的提高.
研究的目的:
- 通过内在标签 (EMKGEL) 提议通过医疗知识图表增强错误检测,这是提高MKG质量的方法.
- 通过利用标签内在信息来解决现有的错误检测方法的局限性.
- 提高利用MKG的下游医疗保健应用程序的可靠性和性能.
主要方法:
- 为隐性标签信息构建了一个超视图知识图 (KG),为邻里信息构建了一个三重级别的KG.
- 引入了一个超视图注意力网络 (GAT) 来整合标签消息和邻里数据以进行表示学习.
- 开发了一个结合本地和全球可信度的信任分数,以估计三倍精度.
主要成果:
- 在PharmKG-8k上,EMKGEL的Precision@K值提高了0.7%,在DiseaseKG上提高了6.1%,在DiaKG上提高了3.6%.
- 该方法在三个公开的MKG和一般知识图 (Nell-995) 上显著优于基线方法.
- 拟议的方法有效地利用了标签上的内在信息,以更准确地检测MKG中的错误.
结论:
- 埃姆克吉尔提供了一个强大的解决方案,用于检测MKG中的错误,特别是当没有准确的外部标签时.
- 整合超视图GAT和基于可信度的信任评分可以提高MKG的质量.
- 这项工作有助于建立更可靠,更准确的医疗知识图表,用于先进的医疗保健应用.
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