Indirect estimation of pediatric reference interval via density graph deep embedded clustering
文献类型：期刊论文
| 作者 | Zheng, Jianguo3; Tang, Yongqiang3 ; Peng, Xiaoxia2; Zhao, Jun1; Chen, Rui3; Yan, Ruohua2; Peng, Yaguang2; Zhang, Wensheng3
|
| 刊名 | COMPUTERS IN BIOLOGY AND MEDICINE
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 169页码:10 |
| ISSN号 | 0010-4825 |
| 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 |
| 通讯作者 | Tang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Peng, Yaguang(plwumi@hotmail.com) ; Zhang, Wensheng(zhangwenshengia@hotmail.com) |
| 英文摘要 | 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. |
| WOS关键词 | LABORATORY DATA-BASES ; BLOOD-COUNT ; ALGORITHM |
| 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] |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:001156723700001 |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55649]  |
| 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.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Informat Ctr, Beijing, 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.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。