Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.

Journal: International journal of cardiology
Published Date: Apr 23, 2024

Abstract

BACKGROUND: The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy.

Authors

  • Andrea Bernardini
    Clinical Neurology Unit, Udine University Hospital, Piazzale Santa Maria della Misericordia, 15, 33100 Udine, Italy. Electronic address: bernardini.andrea@spes.uniud.it.
  • Luca Bindini
    Department of Information Engineering, University of Florence, 50139 Florence, Italy.
  • Emilia Antonucci
    Arianna Anticoagulazione Foundation, Bologna, Italy.
  • Martina Berteotti
    Department of Experimental and Clinical Medicine, University of Florence, Italy.
  • Betti Giusti
    Department of Experimental and Clinical Medicine, University of Florence, Italy.
  • Sophie Testa
    Hemostasis and Thrombosis Center, Laboratory Medicine Department, Azienda Socio-Sanitaria Territoriale, Cremona, Italy.
  • Gualtiero Palareti
    Arianna Anticoagulazione Foundation, Bologna, Italy.
  • Daniela Poli
    Department of Experimental and Clinical Medicine, University of Florence, Italy.
  • Paolo Frasconi
    Department of Information Engineering, University of Florence, 50139 Florence, Italy.
  • Rossella Marcucci
    Department of Experimental and Clinical Medicine, University of Florence, Firenze, Italy.

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