ArticleKidney international reports2021
Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.
Article in Kidney international reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- 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
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- Article
- Article
- Interpretable machine learning for the early prediction of acute kidney injury in preterm infants in the neonatal intensive care unit.Renal failure · 2025Article
- Prediction of acute kidney injury after noncardiac surgery in intensive care unit by machine learning.Annals of medicine and surgery (2012) · 2025Article
- Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Review
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach.Journal of clinical medicine · 2024Article
- Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts.Critical care (London, England) · 2024Article
- Interpretable machine learning model for predicting acute kidney injury in critically ill patients.BMC medical informatics and decision making · 2024Article
- Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Machine-learning model for predicting oliguria in critically ill patients.Scientific reports · 2024Article
- Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis.Renal failure · 2023Article
- Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.Health information science and systems · 2023Article
- Global biomarkers trends in acute kidney injury: a bibliometric analysis.Renal failure · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAcute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.
methodsA CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.
resultsOn a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.
conclusionA CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.
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