Development and validation of a deep neural network–based model to predict acute kidney injury following intravenous administration of iodinated contrast media in hospitalized patients with chronic kidney disease: a multicohort analysis
Development and validation of a deep neural network–based model to predict acute kidney injury following intravenous administration of iodinated contrast media in hospitalized patients with chronic kidney disease: a multicohort analysis
复制标题
开发和验证基于深度神经网络的模型，用于预测慢性肾病住院患者静脉注射碘造影剂后的急性肾损伤：多队列分析
DOI：
10.1093/ndt/gfac049
复制
发表时间：
2022
影响因子：
6.1
通讯作者：
Ying-Hao Deng
中科院分区：
文献类型：
--
作者：
Ping Yan;Shao-Bin Duan;Xiao-Qin Luo;Ning-Ya Zhang;Ying-Hao Deng
Abstract. Background. Stratification of chronic kidney disease (CKD) patients (estimated glomerular filtration rate (eGFR)50%)and the accuracy of patients not developing PC-AKI was 99% in the low-risk category in both the internal and external validation cohorts.. Conclusions. A DNN model using routinely available variables can accurately discriminate the risk of PC-AKI of hospitalized CKD patients following intravenous administration of ICM.
