… The first of these used an electronic health record surveillance algorithm that monitored
medication prescribing practices to identify pediatric inpatients at risk for nephrotoxin-mediated …

… the risk of AKI development from EHR data [11-15], both algorithms were … the prediction
of acute kidney injury risk after percutaneous coronary intervention using machine learning …

… best performing machine learning algorithms for predicting AKI using … using readily available
data indicates that incorporating machine learning predictive models into electronic medical …

… We obtained electronic healthcare data from Medical Information Mart for Intensive Care …
machine learning algorithms in predicting the occurrence of AKD patients was compared using …

… We therefore sought to develop a deep-learning-based algorithm that is able to predict …
It could potentially be integrated into hospitals’ electronic health records for real-time patient …

… We develop a fast, simple and less-costly model based on an ensemble learning algorithm,
… effect of non deep learning methods on the AKI prediction task. In practical application, the …

… How can we best predict acute kidney injury following … care hospital and used machine
learning and logistic regression to … algorithm and calculated distances between patients using all …

… novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. … algorithms
for predicting the development of AKI during hospitalization in patients with sepsis. Through …

… a tertiary care, academic hospital electronic medical record (EMR) … machine learning methods
were used to build prediction … The selected algorithms are wide-applied machine learning …

… a deep learning approach for the continuous risk prediction of … treatment 22 , current algorithms
for detecting AKI depend on … size and scope of electronic health record datasets, we now …