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Original Articles
Machine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD)
Sang Heon Suh, Hong Sang Choi, Chang Seong Kim et al.
Received December 30, 2025 Accepted May 28, 2026 Published online August 7, 2026
[Epub ahead of print]
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Development and external validation of an explainable machine learning model for predicting chronic kidney disease progression in the Korean population
Hojun Yu, Yon Su Kim, Kwon Wook Joo et al.
Received February 2, 2026 Accepted April 29, 2026 Published online June 11, 2026
[Epub ahead of print]
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Impact of acute kidney injury labeling strategies in hospitalized patients: suggestion for a dual-alert clinical decision support system
Se-Jin Ahn, Nam-Jun Cho, Inyong Jeong et al.
Received July 23, 2025 Accepted October 20, 2025 Published online December 8, 2025
[Epub ahead of print]
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Machine learning-based utilization of lipid panels in predicting kidney dysfunction
Soie Kwon, Donghwan Yoon, Changhwa Park et al.
Received February 17, 2025 Accepted July 31, 2025 Published online August 22, 2025
[Epub ahead of print]
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Review Articles
Statistical consideration in nephrology research
Ke Xu, Hakmook Kang
Kidney Res Clin Pract.
2025;44(5):741-750. Published online June 10, 2025
DOI:
https://doi.org/10.23876/j.krcp.25.046
Web of Science 3
Cited By 3
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Original Articles
Impact of prolonged elevated heart rate on sepsis-associated acute kidney injury patients: a causal inference and prediction study
Fuxing Deng, Chunlin Zhu, Yaoyuan Cao, Shuangping Zhao
Received August 13, 2024 Accepted January 4, 2025 Published online April 11, 2025
[Epub ahead of print]
Cited By 3
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A novel approach to the relation of multi-pollutant effect and kidney dysfunction: data analysis from the Korean National Environmental Health Survey Cycle 3 (2015–2017)
Inae Lee, Junhyug Noh, Yaerim Kim et al.
Received July 1, 2024 Accepted December 23, 2024 Published online February 19, 2025
[Epub ahead of print]
Cited By 1
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Predictive models for posttransplant diabetes mellitus in kidney transplant recipients using machine learning and deep learning approach: a nationwide cohort study from South Korea
Seoyoung Choi, Mi Ryung Pyo, Sangwoong Kim et al.
Kidney Res Clin Pract.
2025;44(6):984-995. Published online January 9, 2025
DOI:
https://doi.org/10.23876/j.krcp.24.113
Web of Science 2
Cited By 2
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Predicting long-term mortality of patients with postoperative acute kidney injury following noncardiac general anesthesia surgery using machine learning
Bo Yeon Choi, Wona Choi, Jiwon Min et al.
Kidney Res Clin Pract.
2026;45(2):198-209. Published online September 26, 2024
DOI:
https://doi.org/10.23876/j.krcp.24.106
Web of Science 1
Cited By 2
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Conventional machine learning-based prediction models did not outperform the International IgA Nephropathy Prediction Tool
Sehoon Park, Yisak Kim, Chung Hee Baek et al.
Kidney Res Clin Pract.
2025;44(5):802-813. Published online September 12, 2024
DOI:
https://doi.org/10.23876/j.krcp.23.212
Web of Science 2
Cited By 2
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Review Articles
In silico
medicine and -
omics
strategies in nephrology: contributions and relevance to the diagnosis and prevention of chronic kidney disease
Ana Checa-Ros, Antonella Locascio, Nelia Steib et al.
Kidney Res Clin Pract.
2025;44(1):49-57. Published online July 5, 2024
DOI:
https://doi.org/10.23876/j.krcp.23.334
Web of Science 4
Cited By 5
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Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury
Wisit Cheungpasitporn, Charat Thongprayoon, Kianoush B. Kashani
Kidney Res Clin Pract.
2024;43(4):417-432. Published online June 20, 2024
DOI:
https://doi.org/10.23876/j.krcp.23.298
Web of Science 13
Cited By 15
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Original Articles
A machine learning-based approach for predicting renal function recovery in general ward patients with acute kidney injury
Nam-Jun Cho, Inyong Jeong, Yeongmin Kim et al.
Kidney Res Clin Pract.
2024;43(4):538-547. Published online June 17, 2024
DOI:
https://doi.org/10.23876/j.krcp.23.330
Web of Science 5
Cited By 5
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Machine learning-based 2-year risk prediction tool in immunoglobulin A nephropathy
Yujeong Kim, Jong Hyun Jhee, Chan Min Park et al.
Kidney Res Clin Pract.
2024;43(6):739-752. Published online October 27, 2023
DOI:
https://doi.org/10.23876/j.krcp.23.076
Web of Science 3
Cited By 3
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1
Kidney Research
and
Clinical Practice
Print ISSN: 2211-9132
Online ISSN: 2211-9140
Impact Factor
3.5
MOST VIEWED
MOST CITED
10,812
Role of urate transporters in the kidneys and intestine in uric acid homeostasis
9,619
Baseline characteristics and associated factors for hypertension in children with chronic kidney disease: results from the Korean Cohort Study for Outcome in Patients with Pediatric Chronic Kidney Disease study
33,913
The burden of chronic kidney disease in Asia region: a review of the evidence, current challenges, and future directions
55,365
Factors associated with gait speed: results from the RolE of AST120 (Renamezin) in sarCOpenia preVEntion in pRe-dialYsis chronic kidney disease patients (RECOVERY) study
5,514
Medical treatment for diabetic acute kidney disease from 2012 to 2024: advances, prescription trends, and future directions
+more
Cited By
23
The burden of chronic kidney disease in Asia region: a review of the evidence, current challenges, and future directions
Cited By
18
Kidney Health Plan 2033 in Korea: bridging the gap between the present and the future
Cited By
17
Sarcopenia, sarcopenic obesity, and frailty in individuals with chronic kidney disease: a comprehensive review
Cited By
17
Pathogenesis and management of renal fibrosis induced by unilateral ureteral obstruction
Cited By
15
School urinary screening program in Japan: history, outcomes, perspectives
+more
ABOUT
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BROWSE ARTICLES
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