ArticleInternational journal of general medicine2021
Prediction Models for AKI in ICU: A Comparative Study.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 24 citations in OpenAlex.
- Outcomes of acute kidney injury patients with and without chronic kidney disease in intensive care units: a retrospective contrast analysis.BMC medical informatics and decision making · 2026Article
- Machine Learning Prediction and Causal Forest Analysis of Severe Acute Kidney Injury in ICU Patients with COPD: A MIMIC-IV Study.International journal of chronic obstructive pulmonary disease · 2026Article
- Comparative machine learning to predict acute kidney injury in traumatic brain injury: a MIMIC-IV cohort with SHAP interpretation.Frontiers in medicine · 2026Article
- A Machine Learning Method for Predicting Acute Kidney Injury in Patients with Intracranial Hemorrhage.Cell biochemistry and biophysics · 2025Article
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- Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.JAMIA open · 2024Article
- Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning.Pediatric nephrology (Berlin, Germany) · 2024Article
- An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.Patterns (New York, N.Y.) · 2024Article
- Machine-learning model for predicting oliguria in critically ill patients.Scientific reports · 2024Article
- Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery.International journal of nephrology and renovascular disease · 2024Article
- 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
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
- Machine Learning and Antibiotic Management.Antibiotics (Basel, Switzerland) · 2022Article
- Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.Frontiers in surgery · 2022Article
- Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning.Frontiers in medicine · 2022Article
- Predicting Prolonged Length of ICU Stay through Machine Learning.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the performance of models for early prediction of acute kidney injury (AKI) in the Intensive Care Unit (ICU) setting. PATIENTS AND
methodsData were collected from the Medical Information Mart for Intensive Care (MIMIC)-III database for all patients aged ≥18 years who had their serum creatinine (SCr) level measured for 72 h following ICU admission. Those with existing conditions of kidney disease upon ICU admission were excluded from our analyses. Seventeen predictor variables comprising patient demographics and physiological indicators were selected on the basis of the Kidney Disease Improving Global Outcomes (KDIGO) and medical literature. Six models from three types of methods were tested: Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Decision Machine (LightGBM), and Convolutional Neural Network (CNN). The area under receiver operating characteristic curve (AUC), accuracy, precision, recall and F-measure (F1) were calculated for each model to evaluate performance.
resultsWe extracted the ICU records of 17,205 patients from MIMIC-III dataset. LightGBM had the best performance, with all evaluation indicators achieving the highest value (average AUC = 0.905, F1 = 0.897, recall = 0.836). XGBoost had the second best performance and LR, RF, SVM performed similarly (
conclusionLightGBM demonstrated the best capability for predicting AKI in the first 72 h of ICU admission. LightGBM and XGBoost showed great potential for clinical application owing to their high recall value. This study can provide references for artificial intelligence-powered clinical decision support systems for AKI early prediction in the ICU setting.
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