ArticleTransplant international : official journal of the European Society for Organ Transplantation2020
Machine learning to predict transplant outcomes: helpful or hype? A national cohort study.
Article in Transplant international : official journal of the European Society for Organ Transplantation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence-driven kidney organ allocation: systematic review of clinical outcome prediction, ethical frameworks, and decision-making algorithms.BMC nephrology · 2025Pooled it
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant.Frontiers in immunology · 2021Pooled it
- Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026Review
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Protocol for a global electronic Delphi on integrating artificial intelligence into solid organ transplantation.World journal of transplantation · 2026Review
- Assessing Deceased-Donor Kidneys Through Posttransplant Survival Prediction Algorithms.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- Transforming organ donation and transplantation: challenges, innovations, and future directions.Frontiers in transplantation · 2026Review
- Immunosuppressive regimens and long-term kidney transplant outcomes: A dual survival modeling framework.PloS one · 2026Article
- Shaping the Future of AI in Organ Transplantation: Position Paper of the European Society for Organ Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2026Article
- Multimodal deep learning integration for predicting renal function outcomes in living donor kidney transplantation: a retrospective cohort study.International journal of surgery (London, England) · 2026Article
- Should we adopt a "laparoscopy first" strategy? A comparison of 30-day outcomes between converted open from laparoscopic and planned open colectomy for volvulus.Updates in surgery · 2025Article
- Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.Journal of translational medicine · 2025Article
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Development and validation of a 30-day point-scoring risk calculator for small bowel obstruction surgery.Updates in surgery · 2024Article
- Exploring the variable importance in random forests under correlations: a general concept applied to donor organ quality in post-transplant survival.BMC medical research methodology · 2023Article
- Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation.Journal of nephrology · 2023Review
- Machine Learning Models for Predicting Postoperative Outcomes following Skull Base Meningioma Surgery.Journal of neurological surgery. Part B, Skull base · 2022Review
- Machine Learning-Based Prediction of Masked Hypertension Among Children With Chronic Kidney Disease.Hypertension (Dallas, Tex. : 1979) · 2022Article
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Authors and funding
5 authors.
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Abstract
An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C =
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