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Information 2019, 10(3), 100; https://doi.org/10.3390/info10030100

Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method

1
School of Information and Automation Engineering, Università Politecnica delle Marche (uPM), 60131 Ancona, Italy
2
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche (uPM), 60131 Ancona, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 25 February 2019 / Accepted: 1 March 2019 / Published: 6 March 2019
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Abstract

Statistical bivariate numerical modeling is a method to infer an empirical relationship between unpaired sets of data based on statistical distributions matching. In the present paper, a novel efficient numerical algorithm is proposed to perform bivariate numerical modeling. The algorithm is then applied to correlate glomerular filtration rate to serum creatinine concentration. Glomerular filtration rate is adopted in clinical nephrology as an indicator of kidney function and is relevant for assessing progression of renal disease. As direct measurement of glomerular filtration rate is highly impractical, there is considerable interest in developing numerical algorithms to estimate glomerular filtration rate from parameters which are easier to obtain, such as demographic and ‘bedside’ assays data. View Full-Text
Keywords: glomerular filtration rate; isotonic modeling; statistical modeling; numerical algorithm; kidney disease; renal function glomerular filtration rate; isotonic modeling; statistical modeling; numerical algorithm; kidney disease; renal function
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Giles, S.N.; Fiori, S. Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method. Information 2019, 10, 100.

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