Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.

Journal: BMC medical informatics and decision making
PMID: 40312368

Abstract

BACKGROUND AND OBJECTIVE: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of complications in patients with acute myeloid leukemia (AML) using data available at diagnosis. Predictions are made at three time points: 90 days, six months, and one year post-diagnosis. These objectives represent fundamental steps toward the development of a tool to assist clinicians in therapeutic decision-making and provide insights into the risk factors associated with AML complications.

Authors

  • Pedro Pons-Suñer
    ITI, Universitat Politècnica de València, Valencia, Spain. pedropons@iti.es.
  • François Signol
    Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: fsignol@iti.es.
  • Noemi Alvarez
    Hospital Universitario 12 de Octubre, Imas12, Departament of Medicine, Complutense University, Madrid, Spain.
  • Claudia Sargas
    Instituto de Investigación Sanitaria La Fe, Valencia, Spain.
  • Sara Dorado
    Altum Sequencing, s.l., Computer Science and Engineering Department, Carlos III University, Madrid, Spain.
  • Jose Vicente Gil Ortí
    Instituto de Investigación Sanitaria La Fe, Valencia, Spain.
  • Juan A Delgado Sanchis
    ITI, Universitat Politècnica de València, Valencia, Spain.
  • Marta Llop
    Instituto de Investigación Sanitaria La Fe, Valencia, Spain.
  • Laura Arnal
    ITI, Universitat Politècnica de València, Valencia, Spain.
  • Rafael LLobet
    Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: rllobet@iti.upv.es.
  • Juan-Carlos Perez-Cortes
    Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València, 46022 Valencia, Spain.
  • Rosa Ayala
    Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain.
  • Eva Barragán
    Instituto de Investigación Sanitaria La Fe, Valencia, Spain.

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