ArticleScientific reports2023
Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.Frontiers in medicine · 2025Pooled it
- Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled 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
- Article
- Development and validation of a temporal staging-based nomogram for 1-year mortality in sepsis-associated acute kidney injury.Frontiers in cellular and infection microbiology · 2026Article
- Immune cell profiling supports early prediction of sepsis-associated acute kidney disease using a decision tree algorithm.Biomarker research · 2025Article
- Machine learning models for predicting renal injury in patients with gout.Renal failure · 2025Article
- Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach.Health science reports · 2025Article
- Research Progress on Signalling Pathways Related to Sepsis-Associated Acute Kidney Injury in Children.Current issues in molecular biology · 2025Review
- Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.Journal of clinical medicine · 2025Article
- Bio inspired feature selection and graph learning for sepsis risk stratification.Scientific reports · 2025Article
- Identification of Key Genes and Potential Therapeutic Targets in Sepsis-Associated Acute Kidney Injury Using Transformer and Machine Learning Approaches.Bioengineering (Basel, Switzerland) · 2025Article
- Review of research progress in sepsis-associated acute kidney injury.Frontiers in molecular biosciences · 2025Review
- Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches.Frontiers in medicine · 2025Article
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.Archives of academic emergency medicine · 2025Article
- Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.Frontiers in genetics · 2025Article
- Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.Biomedicines · 2024Review
- Sepsis-Associated Acute Kidney Injury: What's New Regarding Its Diagnostics and Therapeutics?Diagnostics (Basel, Switzerland) · 2024Review
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8 authors.
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Abstract
This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2-79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.
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