Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review.

Journal: Seminars in arthritis and rheumatism
PMID: 39226650

Abstract

OBJECTIVE: This study aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions.

Authors

  • Claudia Mendoza-Pinto
    Systemic Rheumatic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Social Security Institute, Puebla, Mexico.
  • Marcial Sánchez-Tecuatl
    Electronics Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico.
  • Roberto Berra-Romani
    Department of Biomedicine, Medicine School, Benemérita Universidad Autónoma de Puebla, Mexico.
  • Iván Daniel Maya-Castro
    Medicine School, Benemérita Universidad Autónoma de Puebla, Mexico. Electronic address: ivan.mayac@alumno.buap.mx.
  • Ivet Etchegaray-Morales
    Department of Rheumatology, Medicine School, Autonomous University of Puebla, Puebla, Mexico.
  • Pamela Munguía-Realpozo
    Systemic Rheumatic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Social Security Institute, Puebla, Mexico.
  • Maura Cárdenas-García
    Cell Physiology Laboratory, Medicine School, Benemérita Universidad Autónoma de Puebla, Mexico. Electronic address: maura.cardenas@correo.buap.mx.
  • Francisco Javier Arellano-Avendaño
    Department of Rheumatology, Benemérita Universidad Autónoma de Puebla, Mexico. Electronic address: francisco.arellanoa@alumno.buap.mx.
  • Mario García-Carrasco
    Department of Rheumatology, Benemérita Universidad Autónoma de Puebla, Mexico.

Keywords

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