Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation
نویسندگان
چکیده
منابع مشابه
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Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network
BACKGROUND Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. METHODS A total of 1,180 patients that ...
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BACKGROUND Glomerular filtration rate (GFR) is essential for renal function evaluation and classification of chronic kidney disease (CKD), while the reference method in children are cumbersome. In the Chinese children, there was no data about GFR measured through plasma or renal clearance of the exogenous markers, and therefore no validated GFR-estimating tools in this population. METHODS We ...
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Glomerular filtration rate (GFR) estimating equations, infrequently used just a decade ago, are now recommended for the evaluation of kidney function for routine clinical care and are routinely reported by the vast majority of clinical laboratories (1 ). Current clinical guidelines recommend estimated GFR (eGFR) based on serum creatinine (eGFRCr) as the initial diagnostic test, and either a mea...
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Capacitive Voltage Transformers (CVTs) and Current Transformers (CTs) are commonly used in high voltage (HV) and extra high voltage (EHV) systems to provide signals for protecting and measuring devices. Transient response of CTs and CVTs could lead to relay mal-operation. To avoid these phenomena, this paper proposes an artificial neural network (ANN) method to correct CTs and CVTs secondary wa...
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ژورنال
عنوان ژورنال: Journal of Translational Medicine
سال: 2020
ISSN: 1479-5876
DOI: 10.1186/s12967-020-02287-y