Contact and support
Need help, have a question, or want to contact the ResearchHub team?
© 2026 ResearchHub. Built for responsible scholarly connection.
Privacy-aware community infrastructureResearch graph
References from Application of interpretable machine learning for early prediction of prognosis in acute kidney injury. Local targets link to admitted publications; unresolved targets remain external evidence.
Association between AKI and long-term renal and cardiovascular outcomes in United States veterans
10.2215/cjn.02440213 · 2014 · External reference
Fluid accumulation, survival and recovery of kidney function in critically ill patients with acute kidney injury
10.1038/ki.2009.159 · 2009 · External reference
Long-term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and meta-analysis
10.1053/j.ajkd.2008.11.034 · 2009 · External reference
Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study
10.1007/s00134-015-3934-7 · 2015 · External reference
Model to predict mortality in critically ill adults with acute kidney injury
10.2215/cjn.02900311 · 2011 · External reference
Remodeling the model for end-stage liver disease for predicting mortality risk in critically ill patients with cirrhosis and acute kidney injury
10.1002/hep4.1076 · 2017 · External reference
Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model
10.1016/j.ijmedinf.2019.02.002 · 2019 · External reference
Development and validation of a risk stratification model for predicting the mortality of acute kidney injury in critical care patients
10.21037/atm-20-5723 · 2021 · External reference
Clinical applications of machine learning algorithms: beyond the black box
10.1136/bmj.l886 · 2019 · External reference
10.1038/s41561-018-0260-z
10.1038/s41561-018-0260-z · External reference
Opening the Black Box: Interpretable Machine Learning for Geneticists
10.1016/j.tig.2020.03.005 · 2020 · External reference
10.1038/s42256-019-0138-9
10.1038/s42256-019-0138-9 · External reference
Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit
10.14309/ajg.0000000000000632 · 2020 · External reference
Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study
10.1007/s40121-022-00628-6 · 2022 · External reference
Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning
10.1038/s41591-021-01619-9 · 2022 · External reference
10.1186/s13054-020-03179-9
10.1186/s13054-020-03179-9 · External reference
10.1038/sdata.2016.35
10.1038/sdata.2016.35 · External reference
10.1136/bmj.g7594
10.1136/bmj.g7594 · External reference
Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group M. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline
10.7326/0003-4819-158-11-201306040-00007 · 2013 · External reference
Regression shrinkage and selection via the Lasso
10.1111/j.2517-6161.1996.tb02080.x · 1996 · External reference
A tutorial on Support Vector Machines for pattern recognition
10.1023/a:1009715923555 · 1998 · External reference
Efficient kNN Classification With Different Numbers of Nearest Neighbors
2018 · External reference
A Survey of Decision Tree Classifier Methodology
10.1109/21.97458 · 1991 · External reference
The empirical study of the naive Bayes classifier in the case of Markov chain recognition task
10.1007/3-540-32390-2_38 · 2005 · External reference
10.1080/01431160412331269698
10.1080/01431160412331269698 · External reference
Logistic-Regression Diagnostics
10.1214/aos/1176345513 · 1981 · External reference
A Unified Approach to Interpreting Model Predictions
2017 · External reference
Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model
10.1371/journal.pone.0246306 · 2021 · External reference
Predicting mortality among critically ill patients with acute kidney injury treated with renal replacement therapy: Development and validation of new prediction models
10.1016/j.jcrc.2019.12.015 · 2020 · External reference
Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach
10.1038/s41598-021-03894-5 · 2021 · External reference
Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review
10.1371/journal.pone.0169341 · 2017 · External reference
A new scoring model for the prediction of mortality in patients with acute kidney injury
10.1038/s41598-017-08440-w · 2017 · External reference
Mortality prediction in patients with acute kidney injury requiring renal replacement therapy after cardiac surgery
10.1016/j.medici.2017.06.003 · 2017 · External reference
Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury
10.3389/fendo.2021.737996 · 2021 · External reference
Frequency, Predictors, and Outcomes of Prehospital and Early Postarrival Neurological Deterioration in Acute Stroke: Exploratory Analysis of the FAST-MAG Randomized Clinical Trial
10.1001/jamaneurol.2018.1893 · 2018 · External reference
A comparison of the Full Outline of Unresponsiveness (FOUR) and Glasgow Coma Scale (GCS) Scores in Predicting Mortality Among Patients with Reduced Level of Consciousness in Uganda
10.1007/s12028-019-00806-4 · 2020 · External reference
10.1681/asn.2014070724
10.1681/asn.2014070724 · External reference
