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English(EN) SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation

新框架SMI改进了语言模型中医学概念的表示

研究人员开发了一个名为语义医学标识符(SMI)的新框架,以改进语言模型中医学概念的表示。SMI解决了当前模型常常无法捕捉医学术语之间层次结构(特别是稀有或细粒度概念)的局限性。通过将语义ID基于专家定义的医学本体,SMI聚合了冻结的生物医学语言模型的嵌入,以创建保留粗细粒度临床语义的表示。在MIMIC-IV和eICU等临床数据集上的实验表明,SMI在死亡率预测、再入院预测和药物推荐等任务中提高了性能,并在分布变化下产生了更鲁棒的患者表示。 AI

影响 通过改进语言模型中医学概念的语义理解,增强了临床任务的性能。

排序理由 该集群包含一篇详细介绍医学概念表示新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新框架SMI改进了语言模型中医学概念的表示

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该集群包含一篇详细介绍医学概念表示新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ziyang Song, Lia Shen, Yixuan Li, Qincheng Lu, Weihao Li, Haoran Xu, He Zhu, Xinyu Wang, Yue Li ·

    SMI:面向层级感知医学概念表示的本体驱动语义ID

    arXiv:2610.18672v1 Announce Type: cross Abstract: Biomedical language models (LMs) provide strong representations for clinical tasks, but they often fail to capture the hierarchical structure among medical concepts. This limitation is especially pronounced for rare and fine-grain…