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Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction
Liping Wang; Qiang Liu; Mengqi Zhang; Yaxuan Hu; Shu Wu; Liang Wang
Source PublicationIEEE Transactions on Knowledge and Data Engineering (TKDE)
ISSN1041-4347
2023-08-31
Volume36Issue:4Pages:1773-1784
Corresponding AuthorWu, Shu(shu.wu@nlpr.ia.ac.cn)
Subtype期刊论文
Abstract

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.

KeywordMedical diagnostic imaging Knowledge graphs Ontologies Codes Data models Predictive models Graph neural networks Diagnosis prediction electronic health record knowledge graph relational graph neural network
DOI10.1109/TKDE.2023.3310478
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:001181467200031
PublisherIEEE COMPUTER SOC
Sub direction classification机器学习
planning direction of the national heavy laboratory智能计算与学习
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57468
Collection模式识别实验室
Corresponding AuthorShu Wu
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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