Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.

Journal: BMC medicine
PMID:

Abstract

BACKGROUND: Assessing dietary phenylalanine (Phe) tolerance is crucial for managing hyperphenylalaninemia (HPA) in children. However, traditionally, adjusting the diet requires significant time from clinicians and parents. This study aims to investigate the development of a machine-learning model that predicts a range of dietary Phe intake tolerance for children with HPA over 10 years following diagnosis.

Authors

  • Yajie Su
    Centre for Molecular Medicine, Children's Hospital of Fudan University, and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
  • Yaqiong Wang
    Centre for Molecular Medicine, Children's Hospital of Fudan University, and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
  • Jinfeng He
    Department of Neonatology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
  • Huijun Wang
    The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.
  • Xian A
    Department of Neonatology, Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children's Hospital, Urumqi, China.
  • Haili Jiang
    Department of Neonatology, Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children's Hospital, Urumqi, China.
  • Wei Lu
    Department of Pharmacy, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
  • Wenhao Zhou
    The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.
  • Long Li
    Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.