Identifying progressive CKD from healthy population using Bayesian network and artificial intelligence: A worksite-based cohort study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://www.nature.com/articles/s41598-019-41663-7.pdf
Reference19 articles.
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2. Masakane, I. et al. Annual Dialysis Data Report 2015, JSDT Renal Data Registry. Renal Replacement Therapy 4, 1–99 (2018).
3. Inaguma, D. et al. Risk factors for CKD progression in Japanese patients: findings from the Chronic Kidney Disease Japan Cohort (CKD-JAC) study. Clin Exp Nephrol 21, 446–456, https://doi.org/10.1007/s10157-016-1309-1 (2017).
4. Nakayama, M. et al. Increased risk of cardiovascular events and mortality among non-diabetic chronic kidney disease patients with hypertensive nephropathy: the Gonryo study. Hypertens Res 34, 1106–1110, https://doi.org/10.1038/hr.2011.96 (2011).
5. Qaseem, A. et al. Screening, monitoring, and treatment of stage 1 to 3 chronic kidney disease: A clinical practice guideline from the American College of Physicians. Ann Intern Med 159, 835–847, https://doi.org/10.7326/0003-4819-159-12-201312170-00726 (2013).
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