Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.

Journal: Clinical and translational gastroenterology
PMID: 39569890

Abstract

INTRODUCTION: Pediatric Crohn's disease (CD) easily progresses to an active disease compared with adult CD, making it important to predict and minimize CD relapses. However, prediction of relapse at various time points (TPs) during pediatric CD remains understudied. We aimed to develop a real-time aggregated model to predict pediatric CD relapse in different TPs and time windows (TWs).

Authors

  • Sooyoung Jang
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.
  • JaeYong Yu
    Research Institute for Data Science and Artificial Intelligence, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea.
  • Sowon Park
    Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Yonsei University College of Medicine, Severance Fecal Microbiota Transplantation Center, Severance Hospital, Seoul, Republic of Korea .
  • Hyeji Lim
    Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Yonsei University College of Medicine, Severance Fecal Microbiota Transplantation Center, Severance Hospital, Seoul, Republic of Korea .
  • Hong Koh
    Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Yonsei University College of Medicine, Severance Fecal Microbiota Transplantation Center, Severance Hospital, Seoul, Republic of Korea .
  • Yu Rang Park
    Asan Medical Center, Seoul, Republic of Korea.

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