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Structure-aware siamese graph neural networks for encounter-level patient similarity learning

文献类型：期刊论文

作者Gu, Yifan2,3; Yang, Xuebing2; Tian, Lei2,3; Yang, Hongyu5,6; Lv, Jicheng5,6; Yang, Chao5,6; Wang, Jinwei5,6; Xi, Jianing7; Kong, Guilan1,4; Zhang, Wensheng2,3
刊名JOURNAL OF BIOMEDICAL INFORMATICS
出版日期2022-03-01
卷号127页码:13
关键词Encounter-Level Patient Similarity Representation Learning Siamese Networks Graph Neural Networks
ISSN号1532-0464
DOI10.1016/j.jbi.2022.104027
通讯作者Kong, Guilan(guilan.kong@hsc.pku.edu.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要Patient 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.
资助项目National 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]
WOS研究方向Computer Science ; Medical Informatics
语种英语
WOS记录号WOS:000772252000017
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; CAMS Innovation Fund for Medical Sciences
源URL[http://ir.ia.ac.cn/handle/173211/48222]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Kong, Guilan; Zhang, Wensheng
作者单位1.Peking Univ, Natl Inst Hlth Data Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Univ Chinese Acad 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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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