An artificial intelligence-driven predictive model for pediatric allogeneic hematopoietic stem cell transplantation using clinical variables.

Journal: European journal of haematology
Published Date: Feb 9, 2024

Abstract

BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision-making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT.

Authors

  • Carlos Echecopar
    Pediatric Hemato-Oncology, La Paz University Hospital, Madrid, Spain.
  • Inés Abad
    Pediatric Department, Autonomous University of Madrid, Madrid, Spain.
  • Víctor Galán-Gómez
    Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain.
  • Yasmina Mozo Del Castillo
    Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain.
  • Luisa Sisinni
    Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain.
  • David Bueno
    Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain.
  • Beatriz Ruz
    Institute of Medical and Molecular Genetics (INGEMM) idiPAZ Research Institute, La Paz University Hospital, Madrid, Spain.
  • Antonio Pérez-Martínez
    Institute for Health Research, La Paz University Hospital, Madrid, Spain.

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