ArticleClinical kidney journal2021
Predicting mortality in hemodialysis patients using machine learning analysis.
Article in Clinical kidney journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- The prediction of all-cause mortality in end-stage kidney disease patients using social determinants of health: A machine learning framework.Informatics in medicine unlocked · 2026Article
- Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.Scientific reports · 2025Observational
- 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
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.BMC nephrology · 2025Article
- Predicting mortality in elderly chronic kidney disease patients using algorithmic risk assessment: a Southeast Asian perspective.BMC nephrology · 2025Article
- An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.Scientific reports · 2025Article
- Using Machine Learning to Identify Geographic and Socioeconomic Disparities in Dialysis Facility Outcomes Across the United States.Ochsner journal · 2025Article
- Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.Scientific reports · 2024Article
- External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network.Clinical kidney journal · 2024Article
- Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.Journal of clinical medicine · 2024Article
- Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.BMC medical informatics and decision making · 2024Article
- Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.BMC nephrology · 2023Article
- Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.Scientific reports · 2023Observational
- A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.Kidney360 · 2022Article
- Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis.Therapeutic advances in chronic disease · 2022Article
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9 authors.
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Abstract
backgroundBesides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients.
methodsData were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session.
resultsThere were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68-0.73] and superior to logistic regression models (ΔAUC 0.007-0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables.
conclusionsRandom forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.
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