Hierarchical knowledge fusion for enhanced health event prediction: Discriminating between frequent and new diseases

IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Bin Pu , Mingjian Yang , Yan Kang , Guanyuan Chen , Pengchen Liang
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引用次数: 0

Abstract

The prediction of future diseases from patients’ historical Electronic Health Records (EHRs) is of great importance for promoting patient empowerment and preventive healthcare. However, existing studies often overlook the distinction between frequent diseases and new diseases, as well as the complex and hidden relationships among diseases and patients. To address these issues, we propose HKLHEP, a novel hierarchical knowledge-learning algorithm that models health event prediction from both disease and patient perspectives. The method extracts and represents frequent and new diseases within a dynamic graph framework and enriches disease embeddings through a category tree aggregation approach; it further captures both high-level and low-level patient features in a patient-centric manner, evaluates the temporal significance of patient visits by designing a time attention mechanism, and incorporates discharge summaries via transfer learning to enhance textual representations. Experimental results on two large-scale real-world EHR datasets demonstrate that HKLHEP outperforms 11 state-of-the-art methods in health event prediction. The source code is available at https://github.com/yangCode-res/HKLHEP.
层次知识融合增强健康事件预测:多发病和新发疾病的区分
从患者的历史电子健康记录(EHRs)中预测未来疾病对促进患者赋权和预防保健具有重要意义。然而,现有的研究往往忽视了多发病与新发病的区别,以及疾病与患者之间复杂而隐蔽的关系。为了解决这些问题,我们提出了一种新的层次知识学习算法HKLHEP,它从疾病和患者的角度对健康事件预测进行建模。该方法在动态图框架中提取和表示常见疾病和新疾病,并通过类别树聚合方法丰富疾病嵌入;它以患者为中心的方式进一步捕捉高水平和低水平的患者特征,通过设计时间注意机制来评估患者就诊的时间重要性,并通过迁移学习结合出院摘要来增强文本表示。在两个大规模现实世界电子病历数据集上的实验结果表明,HKLHEP在健康事件预测方面优于11种最先进的方法。源代码可从https://github.com/yangCode-res/HKLHEP获得。
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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