AIMC Topic: Creatinine

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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms.

Ecotoxicology and environmental safety
The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal function on gestational anemia remain inconclusive. The concentrations of 20 blood essential trace and no...

Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection.

ACS sensors
By utilizing the synergistic effects of a dual-metal cobalt@copper electrode and advanced machine learning algorithms, we have developed a reliable and cost-effective electrochemical sensor for creatinine monitoring. The sensor's active surface was f...

Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates.

Scientific reports
Therapeutic hypothermia (TH) significantly reduces mortality and morbidities in neonates with Neonatal Encephalopathy (NE). NE may result in neonatal death and multisystem organ impairment, including acute kidney injury (AKI). Our study aimed to util...

Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.

Clinical and translational science
Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center r...

Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely...

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

Clinical nephrology
BACKGROUND: Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPI) equation without race and the estimated glomerul...

End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
BACKGROUND: The extent of interstitial fibrosis in the kidney not only correlates with renal function at the time of biopsy but also predicts future renal outcome. However, its assessment by pathologists lacks good agreement. The aim of this study is...

A Deep Learning Program to Predict Acute Kidney Injury.

Studies in health technology and informatics
Acute kidney injury is a dangerous and sometime fatal clinical situation, which can cause irreversible damage. If we can predict it earlier and make appropriate prevention before its outbreak, kidney injury could be avoided. One challenge of early re...

Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.

Archives of pathology & laboratory medicine
CONTEXT.—: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI.

A Machine Learning Approach to Estimate the Glomerular Filtration Rate in Intensive Care Unit Patients Based on Plasma Iohexol Concentrations and Covariates.

Clinical pharmacokinetics
OBJECTIVE: This work aims to evaluate whether a machine learning approach is appropriate to estimate the glomerular filtration rate in intensive care unit patients based on sparse iohexol pharmacokinetic data and a limited number of predictors.