SynthesisJournal of nephrology2022
Prediction of chronic kidney disease and its progression by artificial intelligence algorithms.
Synthesis in Journal of nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed, 48 citations in OpenAlex.
- Bioinspired hybrid optimisation and deep belief neural networks for early chronic kidney disease detection: an explainable clinical AI framework.Renal failure · 2026Article
- Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.BMC medical informatics and decision making · 2026Review
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A Retrospective Study.Health science reports · 2026Article
- Endocrine disorders linked to chronic kidney disease: Mechanisms and clinical implications.World journal of nephrology · 2026Review
- Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.Journal of medical systems · 2025Article
- A population based optimization of convolutional neural networks for chronic kidney disease prediction.Scientific reports · 2025Article
- Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.Scientific reports · 2025Article
- Observational
- Predicting deep vein thrombosis using machine learning and blood routine analysis.Frontiers in big data · 2025Article
- Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate.Scientific reports · 2024Article
- Brief review for "Machine learning-based 2-year risk prediction tool in immunoglobulin A nephropathy".Kidney research and clinical practice · 2024Article
- A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease.Kidney diseases (Basel, Switzerland) · 2024Article
- Knowledge and Perception of Artificial Intelligence among Faculty Members and Students at Batterjee Medical College.Journal of pharmacy & bioallied sciences · 2024Article
- Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma.BMC medical informatics and decision making · 2024Article
- The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients.BMC medical informatics and decision making · 2023Article
- Expectation of clinical decision support systems: a survey study among nephrologist end-users.BMC medical informatics and decision making · 2023Article
- Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data.Evidence-based complementary and alternative medicine : eCAM · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
objectiveAim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and its progression.
methodsWe conducted a systematic review and selected 55 articles on the application of MLAs in CKD. PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms.
resultsMLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians.
conclusionsThe application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.
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What OpenQuestion holds
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.