Acute Leukemia Warning Model Combined CBC and CPD Data Based on Machine Learning.

Journal: International journal of laboratory hematology
Published Date: Aug 5, 2025

Abstract

BACKGROUND: Early diagnosis plays a crucial role in improving the survival rate of acute leukemia (AL) patients. This study aims to develop a warning model for the detection of acute leukemia (AL) using complete blood count (CBC) and cell population data (CPD), which could aid in clinical diagnosis.

Authors

  • Hong-Wei Gao
    Laboratory Medicine Center, The Second Hospital of Lanzhou University, Lanzhou, China.
  • Ying-Ying Wang
    Northeast Agricultural University Harbin 150030 China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Zhen-Hua Liu
    Department of Pharmacy, Guizhou Orthopedic Hospital, Guiyang, China.
  • Jiang-Ying Cai
    Laboratory Medicine Center, The Second Hospital of Lanzhou University, Lanzhou, China.
  • Wan-Xia Yang
    Laboratory Medicine Center, The Second Hospital of Lanzhou University, Lanzhou, China.
  • Fang-Fang Wang
    Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
  • Zhi-Peng Sun
    Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China.
  • Chong-Ge You
    Laboratory Medicine Center, The Second Hospital of Lanzhou University, Lanzhou, China.

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