TY - JOUR
T1 - A Deep Learning Approach for the Estimation of Glomerular Filtration Rate
AU - Wang, Haishuai
AU - Bowe, Benjamin
AU - Cui, Zhicheng
AU - Yang, Hong
AU - Joshua Swamidass, S.
AU - Xie, Yan
AU - Al-Aly, Ziyad
N1 - Publisher Copyright:
IEEE
PY - 2022
Y1 - 2022
N2 - An accurate estimation of glomerular filtration rate (GFR) is clinically crucial for kidney disease diagnosis and predicting the prognosis of chronic kidney disease (CKD). Machine learning methodologies such as deep neural networks provide a potential avenue for increasing accuracy in GFR estimation. We developed a novel deep learning architecture, a deep and shallow neural network, to estimate GFR (dlGFR for short) and examined its comparative performance with estimated GFR from Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations. The dlGFR model jointly trains a shallow learning model and a deep neural network to enable both linear transformation from input features to a log GFR target, and non-linear feature embedding for stage of kidney function classification. We validate the proposed methods on the data from multiple studies obtained from the NIDDK Central Database Repository. The deep learning model predicted values of GFR within 30% of measured GFR with 88.3% accuracy, compared to the 87.1% and 84.7% of the accuracy achieved by CKD-EPI and MDRD equations (p=0.051 and p<0.001, respectively). Our results suggest that deep learning methods are superior to equations resulting from traditional statistical methods in estimating glomerular filtration rate. Based on these results, an end-to-end predication system has been deployed to facilitate use of the proposed dlGFR algorithm.
AB - An accurate estimation of glomerular filtration rate (GFR) is clinically crucial for kidney disease diagnosis and predicting the prognosis of chronic kidney disease (CKD). Machine learning methodologies such as deep neural networks provide a potential avenue for increasing accuracy in GFR estimation. We developed a novel deep learning architecture, a deep and shallow neural network, to estimate GFR (dlGFR for short) and examined its comparative performance with estimated GFR from Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations. The dlGFR model jointly trains a shallow learning model and a deep neural network to enable both linear transformation from input features to a log GFR target, and non-linear feature embedding for stage of kidney function classification. We validate the proposed methods on the data from multiple studies obtained from the NIDDK Central Database Repository. The deep learning model predicted values of GFR within 30% of measured GFR with 88.3% accuracy, compared to the 87.1% and 84.7% of the accuracy achieved by CKD-EPI and MDRD equations (p=0.051 and p<0.001, respectively). Our results suggest that deep learning methods are superior to equations resulting from traditional statistical methods in estimating glomerular filtration rate. Based on these results, an end-to-end predication system has been deployed to facilitate use of the proposed dlGFR algorithm.
KW - Chronic Kidney Disease
KW - Deep Learning
KW - Deep learning
KW - Diseases
KW - Estimation
KW - Glomerular Filtration Rate
KW - Kidney
KW - Mathematical models
KW - Neural networks
KW - Particle measurements
UR - http://www.scopus.com/inward/record.url?scp=85124241944&partnerID=8YFLogxK
U2 - 10.1109/TNB.2022.3147957
DO - 10.1109/TNB.2022.3147957
M3 - Article
C2 - 35100119
AN - SCOPUS:85124241944
JO - IEEE Transactions on Nanobioscience
JF - IEEE Transactions on Nanobioscience
SN - 1536-1241
ER -