ReviewAdvances in chronic kidney disease2021
Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.
Review in Advances in chronic kidney disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Improving pediatric post-acute kidney injury care: considerations for the clinician.Renal failure · 2026Review
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Predicting Acute Kidney Injury with Nephrotoxic Burden in Noncritical Patients: An Internal and External Validation Study.Kidney360 · 2026Article
- Development and evaluation of an AI-based prediction model for chronic kidney disease after cardiac surgery.BMJ digital health & AI · 2026Article
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- 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
- Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.BMC medical informatics and decision making · 2024Article
- A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury.Scientific reports · 2024Article
- Exploring the Potential of Chatbots in Critical Care Nephrology.Medicines (Basel, Switzerland) · 2023Review
- A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients.Surgery · 2023Article
- Predicting in-hospital outcomes of patients with acute kidney injury.Nature communications · 2023Article
- Article
- Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2022Article
- Artificial Intelligence in Acute Kidney Injury Prediction.Advances in chronic kidney disease · 2022Review
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
- 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
2 authors.
Funding
Abstract
Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were developed using baseline (eg, preoperative) data to evaluate AKI risk. Newer models which incorporated baseline as well as evolving data collected during a hospital admission have shown improved predicative abilities. In this review, we will summarize the advances made in AKI risk prediction over the last several years, including a shift toward more dynamic, real-time, electronic medical record-based models. In addition, we will be discussing the role of electronic AKI alerts and decision support tools. Recent studies have demonstrated improved patient outcomes through the use of these tools which monitor for nephrotoxin medication exposures as well as provide kidney focused care bundles for patients at high risk for severe AKI. Finally, we will briefly discuss the pitfalls and implications of implementing these scores, alerts, and support tools.
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Registered trials
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