Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models.

Journal: Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
PMID: 39128707

Abstract

OBJECTIVE: This study aimed to leverage real-world electronic medical record data to develop interpretable machine learning models for diagnosis of Kawasaki disease while also exploring and prioritizing the significant risk factors.

Authors

  • Yifan Duan
    Beijing Key Laboratory of Big Data Technology for Food Safety, School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
  • Ruiqi Wang
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
  • Zhilin Huang
    Children's Hospital of Chongqing Medical University, Chongqing 400014, PR China.
  • Haoran Chen
    Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843.
  • Mingkun Tang
    Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
  • Jiayin Zhou
  • Zhengyong Hu
    Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, PR China.
  • Wanfei Hu
    Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, PR China.
  • Zhenli Chen
    Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, PR China.
  • Qing Qian
    Nanjing Chervon Auto Precision Technology Co., Ltd, Nanjing 211106, China.
  • Haolin Wang
    College of Medical Informatics, Chongqing Medical University, Chongqing 400016, People's Republic of China.

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