Graph-guided deep hashing networks for similar patient retrieval
Gu, Yifan1,2,3; Yang, Xuebing2; Sun, Mengxuan2,3; Wang, Chutong2,3; Yang, Hongyu1,6; Yang, Chao1,6; Wang, Jinwei1,6; Kong, Guilan4,5; Lv, Jicheng1,6; Zhang, Wensheng2,7
Source PublicationComputers in Biology and Medicine
ISSN0010-4825
2024
Volume169Pages:107865
Corresponding AuthorLv, Jicheng(chenglv@263.net) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
Abstract

With the rapid growth and widespread application of electronic health records (EHRs), similar patient retrieval has become an important task for downstream clinical decision support such as diagnostic reference, treatment planning, etc. However, the high dimensionality, large volume, and heterogeneity of EHRs pose challenges to the efficient and accurate retrieval of patients with similar medical conditions to the current case. Several previous studies have attempted to alleviate these issues by using hash coding techniques, improving retrieval efficiency but merely exploring underlying characteristics among instances to preserve retrieval accuracy. In this paper, drug categories of instances recorded in EHRs are regarded as the ground truth to determine the pairwise similarity, and we consider the abundant semantic information within such multi-labels and propose a novel framework named Graph-guided Deep Hashing Networks (GDHN). To capture correlation dependencies among the multi-labels, we first construct a label graph where each node represents a drug category, then a graph convolution network (GCN) is employed to derive the multi-label embedding of each instance. Thus, we can utilize the learned multi-label embeddings to guide the patient hashing process to obtain more informative and discriminative hash codes. Extensive experiments have been conducted on two datasets, including a real-world dataset concerning IgA nephropathy from Peking University First Hospital, and a publicly available dataset from MIMIC-III, compared with traditional hashing methods and state-of-the-art deep hashing methods using three evaluation metrics. The results demonstrate that GDHN outperforms the competitors at different hash code lengths, validating the superiority of our proposal.

KeywordSimilar patient retrieval Deep hashing Graph neural networks Patient representation learning Electronic health records
DOI10.1016/j.compbiomed.2023.107865
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61976212] ; National Key R&D Program of China[2021ZD0111000] ; National Natural Science Foundation of China[U22B2048] ; National Natural Science Foundation of China[81925006] ; National Natural Science Foundation of China[62203437] ; National High Level Hospital Clinical Research Funding (Youth clinical research project of Peking University First Hospital, China)[2022CR89]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; National High Level Hospital Clinical Research Funding (Youth clinical research project of Peking University First Hospital, China)
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS SubjectBiology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS IDWOS:001165828300001
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Sub direction classification人工智能+医疗
planning direction of the national heavy laboratory实体人工智能系统感认知
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56529
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 AuthorLv, Jicheng; Zhang, Wensheng
Affiliation1.Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
2.State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
4.National Institute of Health Data Science, Peking University, Beijing, China
5.Advanced Institute of Information Technology, Peking University, Hangzhou, China
6.Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
7.Guangzhou University, Guangzhou, 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,Sun, Mengxuan,et al. Graph-guided deep hashing networks for similar patient retrieval[J]. Computers in Biology and Medicine,2024,169:107865.
APA Gu, Yifan.,Yang, Xuebing.,Sun, Mengxuan.,Wang, Chutong.,Yang, Hongyu.,...&Zhang, Wensheng.(2024).Graph-guided deep hashing networks for similar patient retrieval.Computers in Biology and Medicine,169,107865.
MLA Gu, Yifan,et al."Graph-guided deep hashing networks for similar patient retrieval".Computers in Biology and Medicine 169(2024):107865.
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