… the complexities of renal dysfunction, we have curated this special issue to delve into the …
of machine learning and artificial intelligence in the context of patients with renal dysfunction…

… in patients with renal dysfunction often goes unrecognized. … about which patients with renal
dysfunction are at elevated risk … statistical modelling and machine learning as the richer and …

… In this study, the machine learning algorithm achieved better predictive outcomes than the
conventional logistic regression, especially in predicting renal function recovery. Our study …

… machine learning techniques. This set of metabolites can separate early CKD stage patents
from normal subjects with high accuracy. Our study illustrates the power of machine learning …

… The present study applied machine learning algorithms to … in machine learning models, such
as support vector machine … machine (LGBM), and extreme gradient boosting machine (XGB…

… Multiple models of machine learning have been compared, … In the current study, we used
machine learning algorithms to … of patients with DKD confirmed by renal biopsy. In addition, we …

… [9] constructed supervised machine learning to predict hepatotoxicity by using EPA … six
machine learning algorithms: classification and regression trees (CART), support vector machines …

… A total of 1,045 renal transplant patients were recruited, 80% of … Among all the machine
learning models, RT performed best … the first study to use machine learning models to predict TSD…

… BUN is also a classic biomarker for evaluating renal function (27). As mentioned above, SCr…
into different groups based on the severity of renal dysfunction in the present study; stage 1 …

… Prognostic significance of acute kidney injury after reperfused ST-elevation myocardial infarction:
synergistic acceleration of renal dysfunction and left ventricular remodeling. J Card Fail …