ArticleKidney international reports2020
Hierarchical Clustering Analysis for Predicting 1-Year Mortality After Starting Hemodialysis.
Article in Kidney international reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.Renal failure · 2025Article
- A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators.BMC nephrology · 2025Article
- Identification of disease subtypes associated with prognosis in patients with intermediate-high-risk pulmonary embolism based on hierarchical cluster analysis.Journal of thoracic disease · 2025Article
- Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.International urology and nephrology · 2025Review
- Risk factors for short-term all-cause mortality in patients with end stage renal disease: a scoping review.BMC nephrology · 2024Article
- Machine-Based Learning Hierarchical Cluster Analysis: Sex-Based Differences in Prognosis Following Resection of Hepatocellular Carcinoma.World journal of surgery · 2023Article
- Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence.Journal of clinical medicine · 2023Article
- Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning.Life (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
introductionFor patients with end-stage renal disease (ESRD), due to the heterogeneity of the population, appropriate risk assessment approaches and strategies for further follow-up remain scarce. We aimed to conduct a pilot study for better risk stratification, applying machine learning-based classification to patients with ESRD who newly started maintenance hemodialysis.
methodsWe prospectively studied 101 patients with ESRD, who were new to maintenance hemodialysis therapy, between August 2016 and March 2018. Baseline values of variables such as blood and urine tests were obtained before the initiation of hemodialysis. Agglomerative hierarchical clustering was conducted with the collected continuous data. The resulting clusters were followed up for the primary outcome of 1-year mortality, as analyzed by the Kaplan-Meier survival curve with log-rank test and the Cox proportional hazard model.
resultsThe participants were divided into 3 clusters (cluster 1,
conclusionIn this proof-of-concept study, hierarchical clustering discovered a subgroup with a higher 1-year mortality at the initiation of hemodialysis. Applying machine learning-derived classification to patients with ESRD may contribute to better risk stratification.
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