Machine learning-based delta check method for detecting misidentification errors in tumor marker tests.

Journal: Clinical chemistry and laboratory medicine
PMID: 38095534

Abstract

OBJECTIVES: Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods.

Authors

  • Hyeon Seok Seok
    Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea.
  • Yuna Choi
    Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Shinae Yu
    Department of Laboratory Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Kyung-Hwa Shin
    Department of Laboratory Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
  • Sollip Kim
    Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Hangsik Shin
    Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.

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