ArticleBMC medical informatics and decision making2022
Machine learning model for predicting acute kidney injury progression in critically ill patients.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.BMC medical informatics and decision making · 2026Article
- Early identification of acute kidney injury progression in critically ill patients with sepsis: interpretable machine learning approach.Clinical kidney journal · 2026Article
- From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.International urology and nephrology · 2026Review
- Application of XGBoost and logistic regression in predicting 90 days mortality for elderly severe acute renal failure patients.Scientific reports · 2026Article
- Digital Transformation in Healthcare and Nephrology.Cureus · 2026Review
- An interpretable machine-learning model for early prediction of acute kidney injury in polytrauma patients.Frontiers in medicine · 2026Article
- Machine learning risk prediction models for medication harm in hospitalised adult patients.Therapeutic advances in drug safety · 2026Article
- Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.BMC medical informatics and decision making · 2025Article
- Prediction Model for Risk of Death in Elderly Critically Ill Patients with Kidney Failure.Medicina (Kaunas, Lithuania) · 2025Article
- Article
- An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.BMC medical informatics and decision making · 2025Article
- Leveraging artificial intelligence for early detection and prediction of acute kidney injury in clinical practice.Frontiers in physiology · 2025Article
- Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.Hematology, transfusion and cell therapy · 2024Article
- Predicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study.Journal of trauma and injury · 2024Article
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.Scientific reports · 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
- Selective Partitioned Regression for Accurate Kidney Health Monitoring.Annals of biomedical engineering · 2024Article
- Machine-learning model for predicting oliguria in critically ill patients.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Comparing to the patients with AKI stage 1/2, the patients with AKI stage 3 have higher in-hospital mortality and risk of progression to chronic kidney disease. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3.
methodsPatients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care, were included. We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve, and precision-recall curves.
resultsWe included 25,711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression. Thus, we build a software based on our data which can predict AKI progression in real time.
conclusionsThe XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research.
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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.