医学图形扩散:混合图形扩散与异质图形卷积网络用于医学文本分类
IEEE journal of biomedical and health informatics
|June 3, 2025
概括
本研究介绍了医疗图谱扩散 (MGD) 模型,以改进医疗文本分类. MGD模型增强了对复杂医疗文件的理解,比现有方法实现了显著的准确性和F1得分的改进.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 文本分类对于从医学文本中提取知识至关重要.
- 现有的方法可能无法完全捕捉医疗文件中的复杂结构关系.
研究的目的:
- 提出一种新的医学图谱扩散 (MGD) 模型,用于增强医学文本分类.
- 在医疗文档中建模复杂的单词级,句子级和单词句子级关系.
主要方法:
- 构建了一个文本异质图表来表示多层次的结构关系.
- 采用图形扩散卷积来重建图形,克服直接邻居的限制.
- 使用异质图卷积网络和多层感知子进行分类.
主要成果:
- MGD模型在各种基准 (长文本,短文本,医学文本) 中在准确性和F1分数方面取得了显著的改进.
- 与代表性的基线方法相比,表现出优异的性能.
- 废弃性研究证实了异质图形结构和扩散图形卷积的显著影响.
结论:
- MGD模型有效地捕获医学文本中复杂的结构信息.
- 图形扩散卷积和异质图形构造是模型性能的关键.
- MGD模型为医学文本分类提供了强大而有效的解决方案.
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