Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction | |
Liping Wang; Qiang Liu; Mengqi Zhang; Yaxuan Hu; Shu Wu; Liang Wang | |
发表期刊 | IEEE Transactions on Knowledge and Data Engineering (TKDE) |
ISSN | 1041-4347 |
2023-08-31 | |
卷号 | 36期号:4页码:1773-1784 |
通讯作者 | Wu, Shu(shu.wu@nlpr.ia.ac.cn) |
文章类型 | 期刊论文 |
摘要 | Recently, Electronic Health Records (EHR) have become valuable for enhancing medical decision making, as well as online disease detection and monitoring. Meanwhile, deep learning-based methods have achieved great success in health risk prediction and diagnosis prediction based on EHR. Nevertheless, deep learning-based models usually require high volumes of data due to the vast amount of parameters. In addition, a considerable proportion of medical codes appear rarely in the EHR data which poses huge difficulties for clinical applications. Hence, some works propose to adopt medical ontologies to enhance the prediction performance and provide interpretable prediction results. However, these medical ontologies are often small-scale and coarse-grained, most of diagnoses and medical concepts are not included, lacking many diagnoses and medical concepts, let alone various relationships between these concepts. To overcome this limitation, we propose to incorporate existing large-scale medical knowledge graphs (KGs) into diagnosis prediction and devise a Stage-aware Hierarchical Attentive Relational Network, named HAR. Specifically, for each visit, a personalized sub-KG is extracted from the existing medical KG, on which HAR conducts relation-specific message passing and hierarchical message aggregation to refine representations of nodes that correspond to medical codes in visits. HAR takes the specific stage of a patient's disease progression into consideration, which participates in the computation of relation-level and node-level attention. Extensive experiments on two public datasets demonstrate the effectiveness of HAR in improving both the visit-level precision and code-level accuracy of the diagnosis prediction task. |
关键词 | Medical diagnostic imaging Knowledge graphs Ontologies Codes Data models Predictive models Graph neural networks Diagnosis prediction electronic health record knowledge graph relational graph neural network |
DOI | 10.1109/TKDE.2023.3310478 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:001181467200031 |
出版者 | IEEE COMPUTER SOC |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57468 |
专题 | 模式识别实验室 |
通讯作者 | Shu Wu |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liping Wang,Qiang Liu,Mengqi Zhang,et al. Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE),2023,36(4):1773-1784. |
APA | Liping Wang,Qiang Liu,Mengqi Zhang,Yaxuan Hu,Shu Wu,&Liang Wang.(2023).Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction.IEEE Transactions on Knowledge and Data Engineering (TKDE),36(4),1773-1784. |
MLA | Liping Wang,et al."Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction".IEEE Transactions on Knowledge and Data Engineering (TKDE) 36.4(2023):1773-1784. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Stage-Aware_Hierarch(2088KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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