Leveraging knowledge from electronic health records (EHRs) to predict a patient's condition is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underused due to their difficult contents and complex hierarchies. Recently, hypergraph-based methods have been proposed for document classifications. Directly adopting existing hypergraph methods on clinical notes cannot sufficiently utilize the hierarchy information of the patient, which can degrade clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution. Thus, we propose a taxonomy-aware multi-level hypergraph neural network (TM-HGNN), where multi-level hypergraphs assemble useful neutral words with rare keywords via note and taxonomy level hyperedges to retain the clinical semantic information. The constructed patient hypergraphs are fed into hierarchical message passing layers for learning more balanced multi-level knowledge at the note and taxonomy levels. We validate the effectiveness of TM-HGNN by conducting extensive experiments with MIMIC-III dataset on benchmark in-hospital-mortality prediction.

翻译：[翻译摘要] 利用电子健康记录（EHRs）中的知识来预测患者状况，对于提供适当护理至关重要。患者EHRs中的临床笔记包含医疗保健专业人员提供的宝贵信息，但由于其内容复杂且层级结构难以处理，至今未被充分利用。最近，基于超图的方法已被提出用于文档分类。直接将这些现有超图方法应用于临床笔记，无法充分挖掘患者的层级结构信息，这可能会因（1）频繁出现的中性词汇和（2）分布不均衡的层级结构而导致临床语义信息退化。为此，我们提出了一种基于分类感知的多级超图神经网络（TM-HGNN），其中多级超图通过笔记级和分类级超边将有用的中性词汇与稀有关键词进行聚合，从而保留临床语义信息。构建的患者超图被输入层次化消息传递层，以在笔记级和分类级学习更均衡的多级知识。我们通过在MIMIC-III数据集上针对基准的住院死亡率预测任务进行大量实验，验证了TM-HGNN的有效性。