Predicting 1-Year Mortality After Pediatric Heart Transplantation Using Machine Learning
ML-based models outperform traditional approaches and capture evolving risk profiles across eras, supporting their integration into pediatric HT risk stratification to enhance accuracy and equity in transplant decision-making.
Authors
- Swati Choudhry (ORCID: https://orcid.org/0000-0002-9240-1711)
- Bibhuti B. Das (ORCID: https://orcid.org/0000-0001-9530-8785)
- Gopinath Perumal
- Shriprasad R. Deshpande (ORCID: https://orcid.org/0000-0002-8358-9783)
Institutions
- Children's National (US)
- Baylor College of Medicine (US)
- University of Mississippi Medical Center (US)
- Methodist Children’s Hospital (US)
- Texas Children's Hospital (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-12-12
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102422
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00