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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 | |
发表期刊 | Computers in Biology and Medicine |
ISSN | 0010-4825 |
2024 | |
卷号 | 169页码:107865 |
通讯作者 | Lv, Jicheng(chenglv@263.net) ; Zhang, Wensheng(zhangwenshengia@hotmail.com) |
摘要 | 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. |
关键词 | Similar patient retrieval Deep hashing Graph neural networks Patient representation learning Electronic health records |
DOI | 10.1016/j.compbiomed.2023.107865 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National 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] |
项目资助者 | National 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研究方向 | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
WOS类目 | Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology |
WOS记录号 | WOS:001165828300001 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
七大方向——子方向分类 | 人工智能+医疗 |
国重实验室规划方向分类 | 实体人工智能系统感认知 |
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/173211/56529 |
专题 | 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 |
通讯作者 | Lv, Jicheng; Zhang, Wensheng |
作者单位 | 1.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 |
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 | 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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