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

Jianguo Zheng1, Yongqiang Tang1, Xiaoxia Peng2

  • 1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Computers in Biology and Medicine
|December 22, 2023
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Summary

This study introduces a novel deep graph clustering algorithm to estimate pediatric reference intervals indirectly, addressing a critical gap in China. The method accurately predicts intervals for children, improving clinical decision-making.

Keywords:
Deep neural networksGraph clusteringIndirect estimationMachine learningReference interval

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Clinical Chemistry

Background:

  • Establishing pediatric reference intervals (RIs) is vital for clinical decisions but faces challenges in China.
  • Direct sampling for RIs is resource-intensive and ethically complex.
  • Indirect estimation methods offer a viable alternative for predicting RIs.

Purpose of the Study:

  • To introduce deep graph clustering for the indirect estimation of pediatric reference intervals.
  • To propose a novel Density Graph Deep Embedded Clustering (DGDEC) algorithm.
  • To address the gap in pediatric RIs in China.

Main Methods:

  • Developed the Density Graph Deep Embedded Clustering (DGDEC) algorithm.
  • Incorporated a density feature extractor to enhance sample representation.
  • Constructed an adjacency matrix based on sample similarity for patient grouping and RI estimation.

Main Results:

  • The DGDEC algorithm demonstrated superior performance in estimating pediatric RIs compared to other indirect methods.
  • Predicted RIs were closer to true values across different pediatric age and gender groups.
  • Ablation experiments confirmed the effectiveness of patient similarity and multi-scale density features in describing health status.

Conclusions:

  • Deep graph clustering, specifically the DGDEC algorithm, provides an effective approach for indirect estimation of pediatric reference intervals.
  • The method enhances accuracy and generalization, offering a valuable tool for clinical decision-making.
  • The study highlights the importance of patient interrelationships and density features in health status assessment.