ArticleHealthcare informatics research2017
Prediction of Kidney Graft Rejection Using Artificial Neural Network.
Article in Healthcare informatics research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models.Journal of clinical medicine · 2025Review
- The promise of machine learning applications in solid organ transplantation.NPJ digital medicine · 2022Review
- Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network.Journal of healthcare engineering · 2022Article
- Detection of BK polyomavirus-associated nephropathy using plasma graft-derived cell-free DNA: Development of a novel algorithm from programmed monitoring.Frontiers in immunology · 2022Article
- Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions.BioMed research international · 2022Article
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- Artificial Intelligence-related Literature in Transplantation: A Practical Guide.Transplantation · 2021Review
- Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree.Journal of preventive medicine and hygiene · 2021Article
- Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination.Archivos de cardiologia de Mexico · 2021Article
- Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.Scientific reports · 2020Article
- Recent Advances and Clinical Outcomes of Kidney Transplantation.Journal of clinical medicine · 2020Article
- Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.Journal of clinical medicine · 2020Article
- Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review.Journal of clinical medicine · 2020Review
- Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran.BMC research notes · 2019Article
- Augmented intelligence: A synergy between man and the machine.Indian journal of urology : IJU : journal of the Urological Society of IndiaArticle
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesKidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR).
methodsThe study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection.
resultsRecipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR.
conclusionsThe results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical 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.