Machine Learning Models Improve the Diagnostic Yield of Peripheral Blood Flow Cytometry.

Journal: American journal of clinical pathology
Published Date: Jan 2, 2020

Abstract

OBJECTIVES: Peripheral blood flow cytometry (PBFC) is useful for evaluating circulating hematologic malignancies (HM) but has limited diagnostic value for screening. We used machine learning to evaluate whether clinical history and CBC/differential parameters could improve PBFC utilization.

Authors

  • M Lisa Zhang
    Department of Pathology, Massachusetts General Hospital, Boston.
  • Alan X Guo
    Independent Researcher, Boston, MA, Philadelphia.
  • Stephan Kadauke
    Department of Pathology, University of Pennsylvania, Philadelphia.
  • Anand S Dighe
    Department of Pathology, Massachusetts General Hospital, Boston Harvard Medical School, Boston, MA.
  • Jason M Baron
    Department of Pathology, Massachusetts General Hospital, Boston Harvard Medical School, Boston, MA. jmbaron@partners.org.
  • Aliyah R Sohani
    Department of Pathology, Massachusetts General Hospital, Boston.

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