MRHAF:多关系层次关注与混合知识融合,以获得可解释的草药建议
IEEE journal of biomedical and health informatics
|December 15, 2025
概括
这项研究引入了一个新的框架,多关系层次关注与混合知识融合 (MRHAF),通过建模复杂的草药-症状相互作用及其潜在机制来改进传统中医 (TCM) 草药建议.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 医学中的人工智能
背景情况:
- 传统中医药 (TCM) 草药建议使用兼容性原则个性化治疗.
- 现有的方法难以处理草药,效果和症状之间的复杂的多关系相互作用.
- 这限制了功能融合和推性能.
研究的目的:
- 提出一个新的框架,多关系层次关注与混合知识融合 (MRHAF),以加强TCM草药推.
- 通过建模潜伏关系和草药疗效的物质基础,提高预测准确性和可解释性.
- 为了解决当前混合特征融合方法的局限性.
主要方法:
- 开发了三个核心组件的MRHAF:草药有效性-症状知识图 (HESKG),草药-症状交互图 (HSIG) 和草药属性-组件知识图 (HACKG).
- 利用多头和自我注意机制来捕捉全球语义和直接的治疗关联.
- 通过双分支注意力架构集成全球语义和本地交互功能.
主要成果:
- 在两个基准数据集中,MRHAF的表现超过了最先进的基线,在Precision@10中显示了9.75%和22.3%的改进.
- 临床评估证实MRHAF能够捕捉TCM配方原则并提供可靠的建议.
- 网络药理学分析验证了推的草药组合的合理性.
结论:
- 在TCM中,MRHAF提供了一种关于草药兼容性的新视角.
- 该框架提高了草药推中的预测准确性和解释性.
- 在TCM中,MRHAF为临床决策提供了宝贵的指导.
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