Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study.

Abstract

Authors

• Wuchen Yang
  Shanghai Key Lab of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 20093, Shanghai, China. Electronic address: yangmiemieywc@163.com.
• Jingya Liu
  Medical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, China.
• Yang Gou
  Medical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, China.
• Xingqin Huang
  Department of Hematology, Third Military Medical University Southwest Hospital, Chongqing, China.
• Maoshan Chen
  Laboratory of Radiation Biology, Department of Blood Transfusion, Laboratory Medicine Center, Third Military Medical University Second Affiliated Hospital, Chongqing, China.
• Dezhi Huang
  Medical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, China.
• Shengwang Wu
  Medical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, China.
• Jing Zhang
  MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
• Cheng Zhang
  College of Forestry, Jiangxi Agricultural University, Nanchang, Jiangxi Province, China.
• Shuiqing Liu
  Medical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, China liushuiqing062@tmmu.edu.cn pxg1964@136.com zhangxxi@sina.com.
• Xiangui Peng
  Department of Hematology, The Xinqiao Hospital of Army Medical University, Chongqing, China.
• Xi Zhang
  The First Clinical Medical College, Guangxi University of Chinese Medicine, Nanning 530001, China.

Keywords

External Resources