The use of machine learning for the prediction of response to follow-up in spine registries.

Journal: International journal of medical informatics
PMID: 39778467

Abstract

BACKGROUND: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the characteristics of patients who are most likely to drop out, and to evaluate the potential cost effectiveness of the implementation of a follow-up system based on the obtained data.

Authors

  • Alice Baroncini
    Eifelklinik St. Brigida, Abteilung für Wirbelsäulenchirurgie, Simmerath, Germany.
  • Andrea Campagner
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy. Electronic address: a.campagner@campus.unimib.it.
  • Federico Cabitza
    Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy.
  • Francesco Langella
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy.
  • Francesca Barile
    IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
  • Pablo Bellosta-López
    Universidad San Jorge, Campus Universitario, Autov. A23 km 299, 50830 Villanueva de Gállego, Zaragoza, Spain.
  • Domenico Compagnone
    IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
  • Riccardo Cecchinato
    IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy; Department of Biomedical Sciences for Health - University of Milan, Milano, Italy.
  • Marco Damilano
    IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
  • Andrea Redaelli
    IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
  • Daniele Vanni
    IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
  • Pedro Berjano
    IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.

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