ArticleRenal failure2022
Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.
Article in Renal failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Prediction Models for Acute Kidney Injury in Stroke Patients: A Systematic Review.Brain and behavior · 2026Pooled it
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Pooled it
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- Machine learning-based prediction of acute kidney injury after intracerebral hemorrhage: Comparison of multiple model approaches.Medicine · 2025Article
- A Machine Learning Method for Predicting Acute Kidney Injury in Patients with Intracranial Hemorrhage.Cell biochemistry and biophysics · 2025Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Review
- Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.Scientific reports · 2025Observational
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury.Scientific reports · 2024Article
- Explainable Boosting Machine approach identifies risk factors for acute renal failure.Intensive care medicine experimental · 2024Article
- Machine-learning model for predicting oliguria in critically ill patients.Scientific reports · 2024Article
- Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease.American journal of nephrology · 2024Article
- RGX Ensemble Model for Advanced Prediction of Mortality Outcomes in Stroke Patients.BME frontiers · 2024Article
- Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV.International urology and nephrology · 2024Article
- Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting.Frontiers in medicine · 2023Article
- Efficacy of Rosuvastatin Combined with rt-PA Intravenous Thrombolytic Therapy for Elderly Acute Ischemic Stroke Patients.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAcute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with acute cerebrovascular disease.
methodsThis study was a retrospective study based on two different cohorts. Five machine learning methods were used to develop AKI risk prediction models. We used six popular metrics (AUROC, F2-Score, accuracy, sensitivity, specificity and precision) to evaluate the performance of these models.
resultsWe identified 2935 patients in the MIMIC-III database and 499 patients in our local database to develop and validate the AKI risk prediction model. The incidence of AKI in these two different cohorts was 18.3% and 61.7%, respectively. Analysis showed that several laboratory parameters (serum creatinine, hemoglobin, white blood cell count, bicarbonate, blood urea nitrogen, sodium, albumin, and platelet count), age, and length of hospital stay, were the top ten important factors associated with AKI. The analysis demonstrated that the XGBoost had higher AUROC (0.880, 95%CI: 0.831-0.929), indicating that the XGBoost model was better at predicting AKI risk in patients with acute cerebrovascular disease than other models.
conclusionsThis study developed machine learning methods to identify critically ill patients with acute cerebrovascular disease who are at a high risk of developing AKI. This result suggested that machine learning techniques had the potential to improve the prediction of AKI risk models in critical care.
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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 Socratic graph.