ReviewHealthcare (Basel, Switzerland)2021
Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction.
Review in Healthcare (Basel, Switzerland), 2021. 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.
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in contrast-induced nephropathy after coronary interventions: A meta-analysis.Medicine · 2026Pooled it
- 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
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Review
- Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.BMC nephrology · 2024Review
- Interdisciplinary Management of Traumatic Injuries to the Kidneys and Urinary Tract Caused by Blunt Abdominopelvic Trauma.Journal of clinical medicine · 2024Review
- Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.BMC medical informatics and decision making · 2024Article
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Anesthetic Management Recommendations Using a Machine Learning Algorithm to Reduce the Risk of Acute Kidney Injury After Cardiac Surgeries.Anesthesiology and pain medicine · 2024Article
- Assessment of Risk Factors for Acute Kidney Injury with Machine Learning Tools in Children Undergoing Hematopoietic Stem Cell Transplantation.Journal of clinical medicine · 2024Article
- Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction.Plastic and reconstructive surgery. Global open · 2024Article
- KIM-1, IL-18, and NGAL, in the Machine Learning Prediction of Kidney Injury among Children Undergoing Hematopoietic Stem Cell Transplantation-A Pilot Study.International journal of molecular sciences · 2023Article
- Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.BMC medical informatics and decision making · 2023Article
- Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study.Scientific reports · 2022Article
- Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.
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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.