Unlocking treatment success: predicting atypical antipsychotic continuation in youth with mania.

Journal: BMC medical informatics and decision making
PMID: 39095826

Abstract

PURPOSE: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discover potential variables for clinical treatment.

Authors

  • Xiangying Yang
    Mental Health Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P. R. China.
  • Wenbo Huang
  • Li Liu
    Metanotitia Inc., Shenzhen, China.
  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • Song Qing
    Pathology Department of The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Na Huang
    Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan.
  • Jun Zeng
    Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China. Electronic address: zengjun@medmail.com.cn.
  • Kai Yang
    Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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