Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

The Chang Gung Research Database-A multi-institutional electronic medical records database for real-world epidemiological studies in Taiwan

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A Prediction Model for Severe AKI in Critically Ill Adults That Incorporates Clinical and Biomarker Data

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Quality Improvement Goals for Acute Kidney Injury

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Automated Continuous Acute Kidney Injury Prediction and Surveillance: A Random Forest Model

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The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model*

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Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study

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A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography

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Dysregulated Mineral Metabolism in AKI

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Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

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Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

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Predicting AKI in emergency admissions: an external validation study of the acute kidney injury prediction score (APS)

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The risk of chronic kidney disease and mortality are increased after community-acquired acute kidney injury

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KDIGO-based acute kidney injury criteria operate differently in hospitals and the community—findings from a large population cohort

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Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications

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Utilization of Small Changes in Serum Creatinine with Clinical Risk Factors to Assess the Risk of AKI in Critically lll Adults

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Identifying the Patient at Risk of Acute Kidney Injury: A Predictive Scoring System for the Development of Acute Kidney Injury in Acute Medical Patients

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Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach

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