Building the Model.

Journal: Archives of pathology & laboratory medicine
Published Date: Jul 1, 2023

Abstract

CONTEXT.—: Machine learning (ML) allows for the analysis of massive quantities of high-dimensional clinical laboratory data, thereby revealing complex patterns and trends. Thus, ML can potentially improve the efficiency of clinical data interpretation and the practice of laboratory medicine. However, the risks of generating biased or unrepresentative models, which can lead to misleading clinical conclusions or overestimation of the model performance, should be recognized.

Authors

  • He S Yang
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Daniel D Rhoads
    Department of Pathology, Case Western Reserve University, Cleveland, Ohio, USA daniel.rhoads@case.edu.
  • Jorge Sepulveda
    The Department of Pathology, School of Medicine and Health Sciences, George Washington University, Washington, District of Columbia (Sepulveda).
  • Chengxi Zang
    The Department of Population Health Sciences (Zang, Wang), Weill Cornell Medicine, New York, New York.
  • Amy Chadburn
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.

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