ReviewFrontiers in medicine2023
Machine learning for acute kidney injury: Changing the traditional disease prediction mode.
Review in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses 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.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- AI Models for Predicting Acute Kidney Injury (AKI) and Post-AKI Mortality: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The Role of the Cardiothoracic Surgeon in the Age of AI-Are the Robots Going to Take Our Jobs?Medical sciences (Basel, Switzerland) · 2026Pooled it
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Attenuation of sepsis-associated acute lung injury by lncRNA LINC00052 via sponging miR-106b-5p.Journal of cardiothoracic surgery · 2026Article
- Predicting Acute Kidney Injury with Nephrotoxic Burden in Noncritical Patients: An Internal and External Validation Study.Kidney360 · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Machine Learning Models for Point-of-Care Diagnostics of Acute Kidney Injury.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a real-time prediction model for acute kidney injury in hospitalized patients.Nature communications · 2025Article
- Fuzzy logic nursing tool for early acute kidney injury detection in surgical patients.Frontiers in nephrology · 2025Article
- Article
- Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.Renal failure · 2024Article
- New approaches to acute kidney injury.Clinical kidney journal · 2024Article
- Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.BMC nephrology · 2024Review
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets.Journal of personalized 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
- Prediction of Acute Kidney Injury Following Isolated Coronary Artery Bypass Grafting in Heart Failure Patients with Preserved Ejection Fraction Using Machine Leaning with a Novel Nomogram.Reviews in cardiovascular medicine · 2024Article
- Predicting lung cancer's metastats' locations using bioclinical model.Frontiers in medicine · 2024Article
- A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study.JMIR formative research · 2023Article
- 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
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.
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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.