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AUC for renal prognosis prediction.
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创建时间：
2015-12-02
相关数据集
Data_Sheet_1_Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease.pdf
Background and ObjectivesChronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict. Method
NIAID Data Ecosystem80
Baseline clinical characteristics in each class.
Erythropoiesis-stimulating agent (ESA) resistance is reported in approximately 10% of patients on hemodialysis and is a risk factor for mortality. The BRIGHTEN study conducted on 1,724 non-dialysis pa
NIAID Data Ecosystem40
Summary of subgroup analysis results.
Background The global prevalence of chronic kidney disease (CKD) as a major renal disease is increasing rapidly. The progression of CKD may lead to end-stage renal disease (ESRD). Current diagnostic a
NIAID Data Ecosystem30
Additional file 3 of Nomogram predicting the risk of three-year chronic kidney disease adverse outcomes among East Asian patients with CKD
Additional file 3.
Figshare2021-09-27 更新40
Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis
Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, off
Taylor & Francis Group2025-05-12 更新30
