Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

Journal: Drug design, development and therapy
PMID: 39830784

Abstract

PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.

Authors

  • Ying Zeng
    Tongji University School of Medicine, Tongji University, Shanghai, China.
  • Hong Lu
  • Sen Li
    Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
  • Qun-Zhi Shi
    Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.
  • Lin Liu
    Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
  • Yong-Qing Gong
    a Department of Stomatology , Lianyungang Affiliated Hospital of Xuzhou Medical University , Liangyungang , Jiangsu Province , China.
  • Pan Yan
    Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.

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