Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study.

Journal: Clinical chemistry and laboratory medicine
Published Date: Nov 21, 2023

Abstract

OBJECTIVES: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application.

Authors

  • Yu-Fang Liang
    Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China.
  • Andrea Padoan
    Department of Laboratory Medicine, University-Hospital of Padova, via Giustiniani 2, Padova 35128, Italy.
  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Qing-Tao Wang
    Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China.
  • Mario Plebani
    Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy.
  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.

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