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Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Xueling Shang1, Minglong Zhang2, Dehui Sun3

  • 1Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China.

Clinical Chemistry and Laboratory Medicine
|May 15, 2024
PubMed
Summary
This summary is machine-generated.

A new patient-based real-time quality control (PBRTQC) framework integrates anomaly detection and graph neural networks. This approach significantly improves error detection accuracy and reduces detection time for laboratory testing, offering a data-driven solution.

Keywords:
anomaly detectiongraph neural networkpatient-based real-time quality controlstatistical process control

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Area of Science:

  • Clinical chemistry and laboratory medicine
  • Artificial intelligence in healthcare
  • Data science and analytics

Background:

  • Patient-based real-time quality control (PBRTQC) is an emerging laboratory tool.
  • Existing PBRTQC methods face challenges with imbalanced data and variations.
  • Novel algorithms are needed to enhance error detection in PBRTQC.

Purpose of the Study:

  • To propose an integrated framework combining anomaly detection and graph neural networks for PBRTQC.
  • To improve error detection performance by integrating clinical variables and statistical algorithms.
  • To address data volume imbalance and variation issues in PBRTQC.

Main Methods:

  • Collected patient test results for sodium, potassium, and calcium, along with eight independent variables.
  • Modeled a graph-based anomaly detection network to establish control limits.
  • Simulated proportional and random errors for performance evaluation and compared with five mainstream PBRTQC algorithms.

Main Results:

  • Developed and validated the patient-based graph anomaly detection network for real-time quality control (PGADQC).
  • PGADQC demonstrated more balanced performance for positive and negative biases compared to classic PBRTQC.
  • Achieved significant reductions in the average number of patient samples needed for error detection (ANPed), up to 95% for calcium with a 0.02 bias.

Conclusions:

  • PGADQC is an effective framework for PBRTQC, merging statistical and AI algorithms.
  • The framework enhances error detection in a data-driven manner.
  • PGADQC offers a novel data science perspective for advancing PBRTQC.