Indirect estimation of pediatric reference interval via density graph deep embedded clustering

doi:10.1016/j.compbiomed.2023.107852

	Indirect estimation of pediatric reference interval via density graph deep embedded clustering
	Zheng, Jianguo 1; Tang, Yongqiang1 ; Peng, Xiaoxia 2; Zhao, Jun3 ; Chen, Rui1 ; Yan, Ruohua 2; Peng, Yaguang 2; Zhang, Wensheng1
发表期刊	COMPUTERS IN BIOLOGY AND MEDICINE
ISSN	0010-4825
	2024-02-01
卷号	169 页码:10
通讯作者	Tang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Peng, Yaguang(plwumi@hotmail.com) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
摘要	Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-making, and there is a critical gap of pediatric RIs in China. However, the direct sampling technique for establishing RIs is resource-intensive and ethically challenging. Indirect estimation methods, such as unsupervised clustering algorithms, have emerged as potential alternatives for predicting reference intervals. This study introduces deep graph clustering methods into indirect estimation of pediatric reference intervals. Specifically, we propose a Density Graph Deep Embedded Clustering (DGDEC) algorithm, which incorporates a density feature extractor to enhance sample representation and provides additional perspectives for distinguishing different levels of health status among populations. Additionally, we construct an adjacency matrix by computing the similarity between samples after feature enhancement. The DGDEC algorithm leverages the adjacency matrix to capture the interrelationships between patients and divides patients into different groups, thereby estimating reference intervals for the potential healthy population. The experimental results demonstrate that when compared to other indirect estimation techniques, our method ensures the predicted pediatric reference intervals in different age and gender groups are closer to the true values while maintaining good generalization performance. Additionally, through ablation experiments, our study confirms that the similarity between patients and the multi-scale density features of samples can effectively describe the potential health status of patients.
关键词	Reference interval Reference interval Indirect estimation Indirect estimation Machine learning Machine learning Deep neural networks Deep neural networks Graph clustering Graph clustering
DOI	10.1016/j.compbiomed.2023.107852
关键词[WOS]	LABORATORY DATA-BASES ; BLOOD-COUNT ; ALGORITHM
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62106266] ; National Natural Science Foundation of China[62203437] ; Talent Development Plan for High-level Public Health Technical Personnel Project, China[XKGG-02-03] ; Real World Study Project of Hainan Boao Lecheng Pilot Zone, China (Real World Study Base of NMPA)[HNLC2022RWS010] ; Beijing Nova Program, China[Z211100002121053]
项目资助者	National Natural Science Foundation of China ; Talent Development Plan for High-level Public Health Technical Personnel Project, China ; Real World Study Project of Hainan Boao Lecheng Pilot Zone, China (Real World Study Base of NMPA) ; Beijing Nova Program, 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:001156723700001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55650
专题	多模态人工智能系统全国重点实验室
通讯作者	Tang, Yongqiang; Peng, Yaguang; Zhang, Wensheng
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China 2.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Ctr Clin Epidemiol & Evidence Based Med, Beijing, Peoples R China 3.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Informat Ctr, Beijing, Peoples R 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	Zheng, Jianguo,Tang, Yongqiang,Peng, Xiaoxia,et al. Indirect estimation of pediatric reference interval via density graph deep embedded clustering[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2024,169:10.
APA	Zheng, Jianguo.,Tang, Yongqiang.,Peng, Xiaoxia.,Zhao, Jun.,Chen, Rui.,...&Zhang, Wensheng.(2024).Indirect estimation of pediatric reference interval via density graph deep embedded clustering.COMPUTERS IN BIOLOGY AND MEDICINE,169,10.
MLA	Zheng, Jianguo,et al."Indirect estimation of pediatric reference interval via density graph deep embedded clustering".COMPUTERS IN BIOLOGY AND MEDICINE 169(2024):10.