• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Zhu, Zhichao (Zhu, Zhichao.) | Li, Jianqiang (Li, Jianqiang.) | Xu, Chun (Xu, Chun.) | Jia, Yanhe (Jia, Yanhe.) | Ding, Shujie (Ding, Shujie.)

收录:

EI Scopus

摘要:

The surge in artificial intelligence-driven disease prediction technology is profoundly transforming the educational management experience of medical students and practitioners. This technological advancement not only provides them with cutting-edge knowledge in the field but also enhances their understanding of complex medical data and clinical decision support systems, thereby integrating advanced technology into clinical practice and further advancing the medical field. However, the issue of feature sparsity in electronic medical records (EMRs) has become a major constraint on the advancement of current technologies. Graph Neural Network (GNN) can model the graph structured data and infer missing features, which becomes an effective solution to address the above issue. However, if two nodes are far from each other (cross more than three sentences), the relation among them is difficult to be inferred by GNN, which may loss some useful information for disease prediction. This paper presents a Knowledge-based Attention Network (KAN) for disease prediction, which aims to build relations between long-distance nodes by introducing the knowledge, thus fully using the information and improving the disease prediction accuracy. Specifically, beside linking the entity nodes based on the extracted inherent relations, a medical-related knowledge graph (KG) and a trained relation completion (RC) model are leveraged to infer more potential relations of nodes. Then, constructing the EMR graphs, thereby learning the features of nodes and relations to generate representative embeddings for disease prediction. The results on the real-world dateset demonstrate the superiority of KAN. The advanced principles of KAN infuse new knowledge into the education and management of healthcare professionals, enabling them to learn innovative approaches for leveraging cutting-edge technologies in managing complex medical data. © 2024 IEEE.

关键词:

Electronic health record Graph embeddings Medical education Prediction models Decision support systems Records management Diseases Knowledge graph Graph neural networks

作者机构:

  • [ 1 ] [Zhu, Zhichao]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 2 ] [Li, Jianqiang]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 3 ] [Xu, Chun]Xinjiang University of Finance and Economics, Xinjiang, China
  • [ 4 ] [Jia, Yanhe]Beijng Information Science & Technology University, School of Economics and Management, Beijing, China
  • [ 5 ] [Ding, Shujie]Beijing University of Technology, Faculty of Information Technology, Beijing, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

年份: 2024

页码: 355-358

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:521/4878815
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司