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Zheng J, Tang Y, Peng X, Zhao J, Chen R, Yan R, Peng Y, Zhang W. Indirect estimation of pediatric reference interval via density graph deep embedded clustering. Comput Biol Med 2024; 169:107852. [PMID: 38134750 DOI: 10.1016/j.compbiomed.2023.107852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/10/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
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.
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Affiliation(s)
- Jianguo Zheng
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yongqiang Tang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Xiaoxia Peng
- Center for Clinical Epidemiology and Evidence-Based Medicine, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Jun Zhao
- Information Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Rui Chen
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Ruohua Yan
- Center for Clinical Epidemiology and Evidence-Based Medicine, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Yaguang Peng
- Center for Clinical Epidemiology and Evidence-Based Medicine, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Wensheng Zhang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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Ammer T, Schützenmeister A, Prokosch HU, Rauh M, Rank CM, Zierk J. A pipeline for the fully automated estimation of continuous reference intervals using real-world data. Sci Rep 2023; 13:13440. [PMID: 37596314 PMCID: PMC10439150 DOI: 10.1038/s41598-023-40561-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
Reference intervals are essential for interpreting laboratory test results. Continuous reference intervals precisely capture physiological age-specific dynamics that occur throughout life, and thus have the potential to improve clinical decision-making. However, established approaches for estimating continuous reference intervals require samples from healthy individuals, and are therefore substantially restricted. Indirect methods operating on routine measurements enable the estimation of one-dimensional reference intervals, however, no automated approach exists that integrates the dependency on a continuous covariate like age. We propose an integrated pipeline for the fully automated estimation of continuous reference intervals expressed as a generalized additive model for location, scale and shape based on discrete model estimates using an indirect method (refineR). The results are free of subjective user-input, enable conversion of test results into z-scores and can be integrated into laboratory information systems. Comparison of our results to established and validated reference intervals from the CALIPER and PEDREF studies and manufacturers' package inserts shows good agreement of reference limits, indicating that the proposed pipeline generates high-quality results. In conclusion, the developed pipeline enables the generation of high-precision percentile charts and continuous reference intervals. It represents the first parameter-less and fully automated solution for the indirect estimation of continuous reference intervals.
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Affiliation(s)
- Tatjana Ammer
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Roche Diagnostics GmbH, Penzberg, Germany
| | | | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Manfred Rauh
- Department of Pediatrics and Adolescent Medicine, Universitätsklinikum Erlangen, Loschgestr. 15, 91054, Erlangen, Germany
| | | | - Jakob Zierk
- Department of Pediatrics and Adolescent Medicine, Universitätsklinikum Erlangen, Loschgestr. 15, 91054, Erlangen, Germany.
- Center of Medical Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
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