ArticleJournal of clinical medicine2021
Artificial Intelligence-A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial neural network - an effective tool for predicting the lupus nephritis outcome.BMC nephrology · 2022Trial
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Construction of a deep learning-based predictive model for delayed graft function in kidney transplantation.Current urology · 2026Article
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Smart transplants: emerging role of nanotechnology and big data in kidney and islet transplantation, a frontier in precision medicine.Frontiers in immunology · 2025Review
- Integrating Clinical and Histopathological Data to Predict Delayed Graft Function in Kidney Transplant Recipients Using Machine Learning Techniques.Journal of clinical medicine · 2024Article
- Global trends of delayed graft function in kidney transplantation from 2013 to 2023: a bibliometric analysis.Renal failure · 2024Review
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Smart match: revolutionizing organ allocation through artificial intelligence.Frontiers in artificial intelligence · 2024Review
- Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier.Journal of clinical medicine · 2023Article
- Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.BMC cardiovascular disorders · 2023Article
- The Artificial Neural Network as a Diagnostic Tool of the Risk ofJournal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor's eGFR, recipient's BMI, donor's BMI, and recipient-donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor's age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.
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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 OpenQuestion graph.