Bin Pu , Mingjian Yang , Yan Kang , Guanyuan Chen , Pengchen Liang
{"title":"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","authors":"Bin Pu , Mingjian Yang , Yan Kang , Guanyuan Chen , Pengchen Liang","doi":"10.1016/j.inffus.2025.103741","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><span>https://github.com/yangCode-res/HKLHEP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103741"},"PeriodicalIF":15.5000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hierarchical knowledge fusion for enhanced health event prediction: Discriminating between frequent and new diseases\",\"authors\":\"Bin Pu , Mingjian Yang , Yan Kang , Guanyuan Chen , Pengchen Liang\",\"doi\":\"10.1016/j.inffus.2025.103741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 <span><span>https://github.com/yangCode-res/HKLHEP</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":50367,\"journal\":{\"name\":\"Information Fusion\",\"volume\":\"127 \",\"pages\":\"Article 103741\"},\"PeriodicalIF\":15.5000,\"publicationDate\":\"2025-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Fusion\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1566253525008036\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1566253525008036","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Hierarchical knowledge fusion for enhanced health event prediction: Discriminating between frequent and new diseases
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.
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.