ArticleFrontiers in immunology2023
Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 4 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Prediction Models for Acute Kidney Injury in Stroke Patients: A Systematic Review.Brain and behavior · 2026Pooled 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
- Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.BMC medical informatics and decision making · 2026Article
- Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy.BMC medical informatics and decision making · 2026Article
- Machine learning for prediction of in-ICU mortality in sepsis: an observational study using the MIMIC IV database.Archives of medical science : AMS · 2026Article
- Early risk stratification of sepsis-related liver injury via machine learning: a multicohort study.Frontiers in medicine · 2026Article
- Prediction of hospital mortality in sepsis-associated acute kidney injury using a machine-learning approach: a multicenter study using SHAP interpretability analysis.Clinical kidney journal · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Prediction of all-cause in-hospital mortality after ICU admission and 1-year mortality after discharge of patients with acute kidney injury.Renal failure · 2025Article
- Survival machine learning models for predicting all-cause and case-specific mortality risk in metabolic dysfunction-associated fatty liver disease patients.Scientific reports · 2025Article
- Development of a machine learning-based prediction model for acute kidney injury associated with respiratory failure in the intensive care unit.Clinical and experimental medicine · 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
- Machine learning for grading prediction and survival analysis in high grade glioma.Scientific reports · 2025Article
- Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.BMC gastroenterology · 2025Article
- Individualized drug therapy and survival prediction in ICU patients with acute kidney injury: construction and validation of a nomogram.European journal of medical research · 2025Article
- A machine learning model for prediction of cardiac arrest-associated acute kidney injury in the ICU: an internal and external validation study.Frontiers in medicine · 2025Article
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
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Corrections and comments
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Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). Methods: A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. Results: In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O Conclusions: After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.
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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 OpenQuestion graph.