HypKG：面向精准医疗的基于超图的知识图谱情境化方法
HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare
AI总结：
针对通用知识图谱缺乏患者情境难以支撑精准医疗的问题，提出HypKG框架，通过实体链接关联知识图谱与电子健康记录，结合超图模型和任务引导的超图Transformer学习情境化表示，显著提升医疗预测性能，还可优化知识表示质量。
摘要：
知识图谱（KGs）是语义网的重要产物，被广泛应用于各类领域。医疗健康是知识图谱得到密集应用的领域之一，这是因为医疗数据对知识准确性要求极高，且数据本身具有互联互通的特性。然而，存储通用事实信息的知识图谱往往无法纳入知识的重要情境信息（例如特定患者的状态），而这类信息在精准医疗中至关重要。与此同时，电子健康记录（EHRs）提供了丰富的个人数据，包括各类诊断结果和用药信息，为通用知识图谱提供了天然的情境来源。本文提出HypKG框架，该框架将来自EHR的患者信息整合到知识图谱中，生成情境化的知识表示，以实现准确的医疗预测。我们采用先进的实体链接技术，将通用知识图谱中的相关知识与EHR中的患者信息关联起来，随后利用超图模型结合患者信息对知识进行“情境化”处理。最后，我们采用由下游预测任务引导的超图Transformer，联合学习知识图谱和患者的合适合情境化表示，充分利用知识图谱中的现有知识与EHR中的患者情境。在使用一个大型生物医学知识图谱和两个真实世界EHR数据集的实验中，HypKG在多项评估指标下均展现出医疗预测任务性能的显著提升。此外，通过整合外部情境，HypKG能够学习调整知识图谱中实体和关系的表示，有望提升知识的质量和实际应用价值。
英文摘要：
Knowledge graphs (KGs) are important products of the semantic web, which are widely used in various application domains. Healthcare is one of such domains where KGs are intensively used, due to the high requirement for knowledge accuracy and interconnected nature of healthcare data. However, KGs storing general factual information often lack the ability to account for important contexts of the knowledge such as the status of specific patients, which are crucial in precision healthcare. Meanwhile, electronic health records (EHRs) provide rich personal data, including various diagnoses and medications, which provide natural contexts for general KGs. In this paper, we propose HypKG, a framework that integrates patient information from EHRs into KGs to generate contextualized knowledge representations for accurate healthcare predictions. Using advanced entity-linking techniques, we connect relevant knowledge from general KGs with patient information from EHRs, and then utilize a hypergraph model to "contextualize" the knowledge with the patient information. Finally, we employ hypergraph transformers guided by downstream prediction tasks to jointly learn proper contextualized representations for both KGs and patients, fully leveraging existing knowledge in KGs and patient contexts in EHRs. In experiments using a large biomedical KG and two real-world EHR datasets, HypKG demonstrates significant improvements in healthcare prediction tasks across multiple evaluation metrics. Additionally, by integrating external contexts, HypKG can learn to adjust the representations of entities and relations in KG, potentially improving the quality and real-world utility of knowledge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
