A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.

Journal: BMC medical informatics and decision making
PMID:

Abstract

BACKGROUND: Screening of malignant hematological diseases is of great importance for their diagnosis and subsequent treatment. This study constructed an optimal screening model for malignant hematological diseases based on routine blood cell parameters.

Authors

  • Dehua Sun
    Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, PR China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Jun He
    Institute of Animal Nutrition, Sichuan Agricultural University, Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Key Laboratory of Animal Disease-resistant Nutrition and Feed of China Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Disease-resistant Nutrition of Sichuan Province, Ya'an, 625014, China.
  • Yongjian He
    Department of Clinical Laboratory, Nanfang Hospital, Guangzhou, 516006, China.
  • Haoqin Jiang
    Department of Clinical Laboratory, Huashan Hospital Fudan University, Shanghai, 200040, China.
  • Hong Jiang
    Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Dandan Liu
    Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN.
  • Lu Li
    State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, China.
  • Min Liu
    Department of Critical Care Medicine, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China.
  • Zhigang Mao
    Department of Clinical Laboratory, West China Hospital of Sichuan University, Chengdu, 610044, China.
  • Chenxue Qu
    Department of Clinical Laboratory, Peking University First Hospital, Beijing, China. qucx2012@163.com.
  • Linlin Qu
    Department of Clinical Laboratory, The First Bethune Hospital of Jilin University, Jilin, 130061, China.
  • Ziyong Sun
    Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jianbiao Wang
    Clinical Laboratory, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Wenjing Wu
    Department of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
  • Xuefeng Wang
    Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Ying Xing
    Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jingxian Zhang
    Clinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co, Ltd, Shenzhen, 518057, China.
  • Lei Zheng
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China.
  • Shihong Zhang
    Department of Clinical Laboratory, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510062, China.
  • Bo Ye
    Department of Thoracic Surgery, Thoracic Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200030, China.
  • Ming Guan
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China.