Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.

Journal: BMC psychiatry
PMID: 39962416

Abstract

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattention. This study aims to develop a verifiable and interpretable machine learning model to identify ADHD and its subtypes in children using clinical assessment scales data.

Authors

  • Han Qin
    a Department of Stomatology , Lianyungang Affiliated Hospital of Xuzhou Medical University , Liangyungang , Jiangsu Province , China.
  • Lili Zhang
    Pharmaceutics Department, Institute of Medicinal Biotechnology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100050, PR China.
  • Jianhong Wang
    School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China.
  • Weiheng Yan
    Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing, China.
  • Xi Wang
    School of Information, Central University of Finance and Economics, Beijing, China.
  • Xia Qu
    Department of Child Health Care, Children's Hospital Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Nan Peng
    Department of Child Health Care, Children's Hospital Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.

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