State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database.

Journal: Clinical transplantation
Published Date: Jun 29, 2021

Abstract

PURPOSE: We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT).

Authors

  • Polydoros N Kampaktsis
  • Aspasia Tzani
    Brigham and Women's Hospital Heart and Vascular Center, Harvard Medical School, Boston, MA, USA.
  • Ilias P Doulamis
    Division of Cardiac Surgery, Boston's Children Hospital, Boston, MA, USA.
  • Serafeim Moustakidis
    AIDEAS OÜ, Narva mnt 5, Tallinn, Harju maakond, 10117, Estonia.
  • Anastasios Drosou
    Information Technologies Institute, National Center for Research and Technology, Thessaloniki, Greece.
  • Nikolaos Diakos
    Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.
  • Stavros G Drakos
    Division of Cardiovascular Medicine & Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah Health & School of Medicine, Salt Lake, Utah, USA.
  • Alexandros Briasoulis
    Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA.

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