ReviewAdvances in chronic kidney disease2022
Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.
Review in Advances in chronic kidney disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Development and External Validation of an Interpretable Machine Learning Framework for Predicting Pneumothorax-Associated Acute Kidney Injury: A Multicenter Retrospective Study.Journal of clinical medicine · 2026Article
- Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study.Abdominal radiology (New York) · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Predicting In-Hospital Acute Kidney Injury after Cardiac Surgery Using Machine Learning.Journal of cardiothoracic and vascular anesthesia · 2026Observational
- Machine learning models for predicting renal injury in patients with gout.Renal failure · 2025Article
- Predicting Acute Kidney Injury in Acute Rhabdomyolysis.Journal of clinical medicine · 2025Article
- Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.Journal of medical systems · 2025Article
- Acute Kidney Injury Provider and Survivor Education: Current and Emerging Tools.Advances in kidney disease and health · 2025Review
- Leveraging artificial intelligence for early detection and prediction of acute kidney injury in clinical practice.Frontiers in physiology · 2025Article
- Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning.Renal failure · 2024Article
- Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.BMC nephrology · 2024Review
- Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.Pediatric nephrology (Berlin, Germany) · 2024Review
- Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease.American journal of nephrology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.
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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.