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Exploring optimization algorithms for establishing patient-based real-time quality control models.

Xincen Duan1, Chunyan Zhang1, Xiao Tan1

  • 1Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai 200032, China.

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|January 14, 2024
PubMed
Summary
This summary is machine-generated.

Efficient optimization algorithms like genetic algorithms (GA) and differential evolution (DE) significantly reduce computation time for patient-based real-time quality control (PBRTQC) models compared to grid search (GS). This accelerates the development of advanced PBRTQC applications.

Keywords:
Clinical laboratory managementMetaheuristicsOptimizationPBRTQCRARTQC

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Area of Science:

  • Clinical chemistry
  • Laboratory automation
  • Computational biology

Background:

  • Patient-based real-time quality control (PBRTQC) models require optimization for diverse clinical laboratories.
  • The grid search (GS) algorithm is inefficient for PBRTQC model optimization.
  • Efficient optimization algorithms are crucial for PBRTQC research and implementation.

Purpose of the Study:

  • To compare the efficiency and performance of five optimization algorithms for PBRTQC and regression-adjusted real-time quality control (RARTQC) models.
  • To identify faster and more effective optimization methods for clinical laboratory quality control.

Main Methods:

  • Compared grid search (GS), simulated annealing (SA), genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO).
  • Optimized conventional PBRTQC and RARTQC models for serum alanine aminotransferase and sodium.
  • Evaluated model performance and computation time.

Main Results:

  • GA and DE required significantly less computation time than GS.
  • GS, GA, DE, and PSO yielded models with comparable performance.
  • A trade-off exists between optimization method effectiveness and computation time.

Conclusions:

  • Adopting efficient optimization methods like GA and DE is recommended for establishing PBRTQC and RARTQC models.
  • Faster optimization saves time and computing resources.
  • This enables the development of more complex models and scalable PBRTQC applications.