Structure-aware siamese graph neural networks for encounter-level patient similarity learning
Gu, Yifan1,2; Yang, Xuebing1; Tian, Lei1,2; Yang, Hongyu5,6; Lv, Jicheng5,6; Yang, Chao5,6; Wang, Jinwei5,6; Xi, Jianing7; Kong, Guilan3,4; Zhang, Wensheng1,2
Source PublicationJOURNAL OF BIOMEDICAL INFORMATICS
ISSN1532-0464
2022-03-01
Volume127Pages:13
Corresponding AuthorKong, Guilan(guilan.kong@hsc.pku.edu.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractPatient similarity learning has attracted great research interest in biomedical informatics. Correctly identifying the similarity between a given patient and patient records in the database could contribute to clinical references for diagnosis and medication. The sparsity of underlying relationships between patients poses difficulties for similarity learning, which becomes more challenging when considering real-world Electronic Health Records (EHRs) with a large number of missing values. In the paper, we organize EHRs as a graph and propose a novel deep learning framework, Structure-aware Siamese Graph neural Networks (SSGNet), to perform robust encounter-level patient similarity learning while capturing the intrinsic graph structure and mitigating the influence from missing values. The proposed SSGNet regards each patient encounter as a node, and learns the node embeddings and the similarity between nodes simultaneously via Graph Neural Networks (GNNs) with siamese architecture. Further, SSGNet employs a low-rank and contrastive objective to optimize the structure of the patient graph and enhance model capacity. The extensive experiments were conducted on two publicly available datasets and a real-world dataset regarding IgA nephropathy from Peking University First Hospital, in comparison with multiple baseline and state-of-the-art methods. The significant improvement in Accuracy, Precision, Recall and F1 score on the patient encounter pairwise similarity classification task demonstrates the superiority of SSGNet. The mean average precision (mAP) of SSGNet on the similar encounter retrieval task is also better than other competitors. Furthermore, SSGNet's stable similarity classification accuracies at different missing rates of data validate the effectiveness and robustness of our proposal.
KeywordEncounter-Level Patient Similarity Representation Learning Siamese Networks Graph Neural Networks
DOI10.1016/j.jbi.2022.104027
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018AAA0102100] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61906190] ; National Natural Science Foundation of China[62006139] ; CAMS Innovation Fund for Medical Sciences[2019-I2M-5-046]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; CAMS Innovation Fund for Medical Sciences
WOS Research AreaComputer Science ; Medical Informatics
WOS SubjectComputer Science, Interdisciplinary Applications ; Medical Informatics
WOS IDWOS:000772252000017
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
Sub direction classification人工智能+医疗
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48223
Collection多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队
Corresponding AuthorKong, Guilan; Zhang, Wensheng
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Peking Univ, Natl Inst Hlth Data Sci, Beijing, Peoples R China
4.Peking Univ, Adv Inst Informat Technol, Hangzhou, Peoples R China
5.Peking Univ, Dept Med, Renal Div, Hosp 1, Beijing, Peoples R China
6.Chinese Acad Med Sci, Res Units Diag & Treatment Immune Mediated Kidney, Beijing, Peoples R China
7.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gu, Yifan,Yang, Xuebing,Tian, Lei,et al. Structure-aware siamese graph neural networks for encounter-level patient similarity learning[J]. JOURNAL OF BIOMEDICAL INFORMATICS,2022,127:13.
APA Gu, Yifan.,Yang, Xuebing.,Tian, Lei.,Yang, Hongyu.,Lv, Jicheng.,...&Zhang, Wensheng.(2022).Structure-aware siamese graph neural networks for encounter-level patient similarity learning.JOURNAL OF BIOMEDICAL INFORMATICS,127,13.
MLA Gu, Yifan,et al."Structure-aware siamese graph neural networks for encounter-level patient similarity learning".JOURNAL OF BIOMEDICAL INFORMATICS 127(2022):13.
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