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

Cardiac Disease Management (168) Clinical Information and Decision Making (2158) Cardiology (245) Decision Support for Health Professionals (1707) Machine Learning (2319) Artificial Intelligence (2471)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45352, first published .
Doctor shows child a digital heart model during medical consultation.

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 (10) Tweetations (3) 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
  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
  5. Cohen B, He Z, Gorchs R, Galván N, Goss J, Rana A. A Novel Index to Predict Time to School Attendance After Pediatric Heart Transplant: SAAT Score. Pediatric Cardiology 2026;47(5):2147 View
  6. Verhoeven R, Bouisaghouane W, Hulscher J. Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks. European Journal of Pediatric Surgery 2026;36(03):168 View
  7. Das B, Deshpande S, Choudhry S, Perumal G. Predicting 1-Year Mortality After Pediatric Heart Transplantation Using Machine Learning. JACC: Advances 2026;5(1):102422 View
  8. Abouelmagd K, Saleem N, Kuhn K, Oyedele T, Abdelbar S, Rath S, Alsabri M. Artificial Intelligence and Advanced Technologies in Pediatric Airway Management: Transforming Emergency Care. Current Emergency and Hospital Medicine Reports 2025;13(1) View
  9. Kim D, Madabhushi A, Margulies K, Peyster E. An Integrated Clinical-Histopathologic Prediction Model for Cardiac Allograft Rejection: Translating Machine Learning into Clinical Risk Frameworks. The Journal of Heart and Lung Transplantation 2026 View
  10. Altamimi O, Ahmad A, Badran R, Sleihat Y, Altamimi A, Al‐Ammouri I. Risk Stratification for Graft Failure After Pediatric Heart Transplantation Using Random Survival Forests. Pediatric Transplantation 2026;30(5) View