ArticleScientific reports2024
Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Commentary on "A preoperative nomogram and web-based clinical decision support system for predicting early renal function after living donor kidney transplantation".International journal of surgery (London, England) · 2026Article
- Assessing Deceased-Donor Kidneys Through Posttransplant Survival Prediction Algorithms.American journal of kidney diseases : the official journal of the National Kidney Foundation · 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
- Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction.NPJ digital medicine · 2025Article
- Optimizing kidney allocation: challenges and solutions.Current opinion in organ transplantation · 2025Review
- A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras.Frontiers in digital health · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretable methods to predict both graft failure and patient death using data from previously accepted kidney transplant offers. Using more than 25 years of transplant outcome data, we train and compare several survival analysis models in single risk settings. In addition, we use post hoc interpretability techniques to clinically validate these models. Neural networks show comparable performance to the Cox proportional hazard model, with concordance of 0.63 and 0.79 for prediction of graft failure and patient death, respectively. Donor and recipient ages, the number of mismatches at DR locus, dialysis type, and primary renal disease appear to be important features for transplant outcome prediction. Owing to their good predictive performance and the clinical relevance of their post hoc interpretation, neural networks represent a promising core component in the construction of future decision support systems for transplant offering.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.