ReviewJournal of clinical medicine2020
Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review.
Review in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant.Frontiers in immunology · 2021Pooled it
- Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- Predictors of Long-Term Kidney Transplant Survival: A Scoping Review.Health science reports · 2026Review
- Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study.JMIR cancer · 2026Article
- Article
- The effectiveness, equity and explainability of health service resource allocation-with applications in kidney transplantation & family planning.Frontiers in health services · 2025Article
- Smart transplants: emerging role of nanotechnology and big data in kidney and islet transplantation, a frontier in precision medicine.Frontiers in immunology · 2025Review
- Predicting kidney graft function and failure among kidney transplant recipients.BMC medical research methodology · 2024Article
- Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories.Transplantation direct · 2024Article
- Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study.Frontiers in oncology · 2023Article
- Maximizing utility of nondirected living liver donor grafts using machine learning.Frontiers in immunology · 2023Article
- A novel dynamic Bayesian network approach for data mining and survival data analysis.BMC medical informatics and decision making · 2022Article
- Exfoliated Kidney Cells from Urine for Early Diagnosis and Prognostication of CKD: The Way of the Future?International journal of molecular sciences · 2022Review
- Kidney Transplantation and Diagnostic Imaging: The Early Days and Future Advancements of Transplant Surgery.Diagnostics (Basel, Switzerland) · 2020Review
- Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.Journal of clinical medicine · 2020Article
- Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges.XenotransplantationReview
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
A key issue in the field of kidney transplants is the analysis of transplant recipients' survival. By means of the information obtained from transplant patients, it is possible to analyse in which cases a transplant has a higher likelihood of success and the factors on which it will depend. In general, these analyses have been conducted by applying traditional statistical techniques, as the amount and variety of data available about kidney transplant processes were limited. However, two main changes have taken place in this field in the last decade. Firstly, the digitalisation of medical information through the use of electronic health records (EHRs), which store patients' medical histories electronically. This facilitates automatic information processing through specialised software. Secondly, medical Big Data has provided access to vast amounts of data on medical processes. The information currently available on kidney transplants is huge and varied by comparison to that initially available for this kind of study. This new context has led to the use of other non-traditional techniques more suitable to conduct survival analyses in these new conditions. Specifically, this paper provides a review of the main machine learning methods and tools that are being used to conduct kidney transplant patient and graft survival analyses.
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Registered trials
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