A prediction model for genetic cholestatic disease in infancy using the machine learning approach.

Journal: Journal of pediatric gastroenterology and nutrition
Published Date: Jul 30, 2025

Abstract

OBJECTIVES: Cholestasis in infancy poses a complex clinical conundrum for pediatric hepatologists, warranting timely diagnosis, especially for genetic diseases. This study aims to create machine learning (ML)-based prediction models, referred to as Jaundice Diagnosis Easy for Baby (JADE-B), to identify the subjects prone to genetic causes of cholestasis.

Authors

  • Chi-San Tai
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Sung-Chu Ko
    Center of Intelligent Healthcare, National Taiwan University Hospital, Taipei, Taiwan.
  • Chien-Chang Lee
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Hui-Ru Yang
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Chia-Ray Lin
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Byung-Ho Choe
    Department of Pediatrics, Kyungpook National University Children's Hospital, South Korea.
  • Suporn Treepongkaruna
    Division of Gastroenterology, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
  • Voranush Chongsrisawat
    Division of Gastroenterology and Hepatology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
  • Chau-Chung Wu
  • Huey-Ling Chen
    Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.

Keywords

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