Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation

The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate...

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Veröffentlicht in:	Journal of translational medicine 2020-03, Vol.18 (1), p.120-120, Article 120
Hauptverfasser:	Li, Ningshan, Huang, Hui, Qian, Han-Zhu, Liu, Peijia, Lu, Hui, Liu, Xun
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Age Albumin Antibacterial agents Artificial neural network Artificial neural networks Bias Care and treatment China Chronic kidney disease Chronic kidney failure Cimetidine Computer applications Creatinine Datasets Dialysis Diseases Epidemiology Estimating equation Gender Glomerular Filtration Rate Glycosylated hemoglobin Hemoglobins Humans Kidney diseases Laboratories Medical computer applications Medical research Neural networks Neural Networks, Computer Population Prejudice Prognosis Purines Renal Insufficiency, Chronic Software Statistical analysis Studies Technology Urea Uric acid Variables
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Zusammenfassung:	The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population. The enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age
ISSN:	1479-5876 1479-5876
DOI:	10.1186/s12967-020-02287-y