Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45352, first published .
Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches

Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches

Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches

Journals

  1. Venturini M, Haredasht F, Sabovčik F, Miller R, Kuznetsova T, Vens C. Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches. IEEE Access 2024;12:89754 View
  2. Salih A, Galazzo I, Gkontra P, Rauseo E, Lee A, Lekadir K, Radeva P, Petersen S, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artificial Intelligence Review 2024;57(9) View
  3. Mohammadi I, Farahani S, Karimi A, Jahanian S, Firouzabadi S, Alinejadfard M, Fatemi A, Hajikarimloo B, Akhlaghpasand M. Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis. Frontiers in Artificial Intelligence 2025;8 View
  4. Cousino M, Plevinsky J, Niel K, Rothman E, Schnieder L, Wolfe K, Killian M. Child and adolescent heart and lung post-transplant adherence. JHLT Open 2025;9:100293 View