Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.

Journal: Arthritis research & therapy
Published Date:

Abstract

OBJECTIVE: The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics.

Authors

  • Wenxun Lin
    Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xi Xie
    First Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Guangzhou, 510080, China; State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, 510060, China. Electronic address: xiexi27@mail.sysu.edu.cn.
  • Zhijun Luo
    Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiaoqi Chen
    Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China.
  • Heng Cao
    National Clinical Research Center for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan, China.
  • Xun Fang
    Department of Rheumatology, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China.
  • You Song
    School of Software, Beihang University, Beijing, China.
  • Xujing Yuan
    Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiaojing Liu
    School of Computer, Shenyang Aerospace University, Shenyang 110136, China.
  • Rong Du
    Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.