{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:00:46Z","timestamp":1762351246275,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T00:00:00Z","timestamp":1551830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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 \u2018bedside\u2019 assays data.<\/jats:p>","DOI":"10.3390\/info10030100","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T10:52:22Z","timestamp":1551955942000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3062-6982","authenticated-orcid":false,"given":"Sebastian Nicolas","family":"Giles","sequence":"first","affiliation":[{"name":"School of Information and Automation Engineering, Universit\u00e0 Politecnica delle Marche (uPM), 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5964-7464","authenticated-orcid":false,"given":"Simone","family":"Fiori","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Universit\u00e0 Politecnica delle Marche (uPM), 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1190\/1.1778239","article-title":"Texture model regression for effective feature discrimination: Application to seismic facies visualization and interpretation","volume":"69","author":"Gao","year":"2004","journal-title":"Geophysics"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Carrara, P., Altamura, E., D\u2019Angelo, F., Mavelli, F., and Stano, P. 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