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.

中文翻译：

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估计肾小球滤过率的深度学习方法

准确估计肾小球滤过率 (GFR) 对于肾脏疾病的诊断和预测慢性肾脏病 (CKD) 的预后具有重要的临床意义。深度神经网络等机器学习方法为提高 GFR 估计的准确性提供了潜在途径。我们开发了一种新的深度学习架构，即深度和浅层神经网络，以估计 GFR（简称 dlGFR），并检查其与来自肾脏疾病饮食调整（MDRD）和慢性肾脏病流行病学协作（CKD- EPI）方程。dlGFR 模型联合训练浅层学习模型和深度神经网络，以实现从输入特征到 log GFR 目标的线性转换，以及用于肾功能分类阶段的非线性特征嵌入。我们根据从 NIDDK 中央数据库存储库获得的多项研究数据验证了所提出的方法。深度学习模型预测 GFR 值在测量 GFR 的 30% 以内，准确度为 88.3%，而 CKD-EPI 和 MDRD 方程的准确度分别为 87.1% 和 84.7%（分别为 p = 0.051 和 p < 0.001）。我们的结果表明，深度学习方法在估计肾小球滤过率方面优于传统统计方法得出的方程。基于这些结果，已部署端到端预测系统以促进所提出的 dlGFR 算法的使用。CKD-EPI 和 MDRD 方程实现的准确度分别为 1% 和 84.7%（分别为 p = 0.051 和 p < 0.001）。我们的结果表明，深度学习方法在估计肾小球滤过率方面优于传统统计方法得出的方程。基于这些结果，已部署端到端预测系统以促进所提出的 dlGFR 算法的使用。CKD-EPI 和 MDRD 方程实现的准确度分别为 1% 和 84.7%（分别为 p = 0.051 和 p < 0.001）。我们的结果表明，深度学习方法在估计肾小球滤过率方面优于传统统计方法得出的方程。基于这些结果，已部署端到端预测系统以促进所提出的 dlGFR 算法的使用。