ArticleComputational and structural biotechnology journal2022
Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 49 citations in OpenAlex.
- Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning.Renal failure · 2026Article
- Predicting Chronic Kidney Disease from Biomarkers: An Explainable Machine Learning Approach.Diagnostics (Basel, Switzerland) · 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
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers.NPJ digital medicine · 2026Article
- Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.BMJ health & care informatics · 2026Article
- Article
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- Beyond the Individual: A Data-Driven Approach to Protect Kidneys from Environmental Change.Health data science · 2026Article
- Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis.Renal failure · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Machine Learning-Based Prediction of Muscle Injury Risk in Professional Football: A Four-Year Longitudinal Study.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.Journal of medical Internet research · 2025Article
- Machine learning-derived multivariate renal function trajectories in acute kidney injury in critically ill patients: a multicentre retrospective study.Clinical kidney journal · 2025Article
- An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.Scientific reports · 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
- The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.Depression and anxiety · 2025Article
- Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.Renal failure · 2024Article
- ExplainableJournal of cancer research and clinical oncology · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to develop an algorithm using the explainable artificial intelligence (XAI) approaches for the early prediction of mortality in intensive care unit (ICU) patients with acute kidney injury (AKI). Methods: This study gathered clinical data with AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) in the US between 2008 and 2019. All the data were further randomly divided into a training cohort and a validation cohort. Seven machine learning methods were used to develop the models for assessing in-hospital mortality. The optimal model was selected based on its accuracy and area under the curve (AUC). The SHapley Additive exPlanation (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithm were utilized to interpret the optimal model. Results: A total of 22,360 patients with AKI were finally enrolled in this study (median age, 69.5 years; female, 42.8%). They were randomly split into a training cohort (16770, 75%) and a validation cohort (5590, 25%). The eXtreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.890. The SHAP values showed that Glasgow Coma Scale (GCS), blood urea nitrogen, cumulative urine output on Day 1 and age were the top 4 most important variables contributing to the XGBoost model. The LIME algorithm was used to explain the individualized predictions. Conclusions: Machine-learning models based on clinical features were developed and validated with great performance for the early prediction of a high risk of death in patients with AKI.
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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.