Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate
Felipe Kenji Nakano, Anna Åkesson, Jasper de Boer, Klest Dedja, Robbe D’hondt, Fateme Nateghi Haredasht, Jonas Björk, Marie Courbebaisse, Lionel Couzi, Natalie Ebert, Björn O Eriksen, R Neil Dalton, Laurence Derain-Dubourg, Francois Gaillard, Cyril Garrouste, Anders Grubb, Lola Jacquemont, Magnus Ha · 2024
Read Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate by Felipe Kenji Nakano, Anna Åkesson, Jasper de Boer, Klest Dedja, Robbe D’hondt, Fateme Nateghi Haredasht, Jonas Björk, Marie Courbebaisse, Lionel Couzi, Natalie Ebert, Björn O Eriksen, R Neil Dalton, Laurence Derain-Dubourg, Francois Gaillard, Cyril Garrouste, Anders Grubb, Lola Jacquemont, Magnus Ha (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11530427.txt.
Description
In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is normally calculated using equations, such as the European Kidney Function Consortium (EKFC) equation. Despite being the most general equation, EKFC, just like previously proposed approaches, can still struggle to achieve satisfactory performance, limiting its clinical applicability.
Catalog record
| CREATOR | Felipe Kenji Nakano, Anna Åkesson, Jasper de Boer, Klest Dedja, Robbe D’hondt, Fateme Nateghi Haredasht, Jonas Björk, Marie Courbebaisse, Lionel Couzi, Natalie Ebert, Björn O Eriksen, R Neil Dalton, Laurence Derain-Dubourg, Francois Gaillard, Cyril Garrouste, Anders Grubb, Lola Jacquemont, Magnus Ha |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |