一个基于GCN的方法来发现UMLS Metathesaurus中错位的同义词
Xubing Hao1, Rashmie Abeysinghe2, Jay Shi3
1McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
本研究介绍了一种图形卷积网络 (GCN) 方法,用于在统一医疗语言系统 (UMLS) 中找到不正确的同义词分组. 这种方法有望提高生物医学词汇数据的准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 知识表示 知识表示
背景情况:
- 统一医疗语言系统 (UMLS) 是生物医学应用的关键资源,整合了多样化的词汇.
- 保持UMLS的质量和准确性对于下游生物医学研究和应用的可靠性至关重要.
- 在UMLS中识别错位的同义词是数据质量保证的一个关键挑战.
研究的目的:
- 开发和评估一种基于图形卷积网络 (GCN) 的新方法,用于检测UMLS中错位的同义词.
- 在UMLS知识库中提高同义词分组的准确性.
- 为识别生物医学术语中的潜在错误提供可扩展的计算方法.
主要方法:
- 使用图形卷积网络 (GCN) 模型,在相同的UMLS概念中以同义词进行训练,作为积极样本.
- 使用顶部词汇上类似的术语作为负样本,以训练GCN从类似的术语中区分真实的同义词.
- 将训练的GCN模型应用于测试数据集,以识别可能错位的同义词.
主要成果:
- 该GCN模型确定了147,625个可能错位的同义词.
- 人类专家对100个随机选择的建议进行了评估,证实了60%的准确性,表明了该模型的有效性.
- 该研究证明了GCN在改善大型生物医学术语中的同义词分组质量的潜力.
结论:
- 提出的基于GCN的方法是提高UMLS中同义词分组质量的有希望的方法.
- 这种计算技术可以协助生物医学词汇的持续策划和改进.
- 该方法的进一步开发和应用可能会导致更准确和可靠的UMLS资源.
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