This paper is in the following e-collection/theme issue:

Cardiac Disease Management (100) Clinical Information and Decision Making (1434) Cardiology (164) Decision Support for Health Professionals (1210) Machine Learning (1501) Artificial Intelligence (1421)

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

Authors of this article:

Michael O Killian1 Author Orcid Image ;   Shubo Tian2 Author Orcid Image ;   Aiwen Xing2 Author Orcid Image ;   Dana Hughes1 Author Orcid Image ;   Dipankar Gupta3 Author Orcid Image ;   Xiaoyu Wang2 Author Orcid Image ;   Zhe He4 Author Orcid Image
Article Authors Cited by (2) Tweetations (9) Metrics

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