ArticleJMIR medical informatics2020
Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Development and validation of a machine learning-based prediction model for malnutrition risk in peritoneal dialysis patients: a multi‑center retrospective study.International urology and nephrology · 2026Article
- Predicting cardiovascular events in hemodialysis patients based on the fusion of physicochemical indicators and tongue images: a prospective and multicenter study.Frontiers in physiology · 2026Article
- Development and validation of interpretable machine learning models to predict intensive care unit outcomes in patients on hemodialysis: a multicenter study.BMC medical informatics and decision making · 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
- Predicting mortality in elderly chronic kidney disease patients using algorithmic risk assessment: a Southeast Asian perspective.BMC nephrology · 2025Article
- Risk prediction for cardiovascular events and all-cause mortality in maintenance hemodialysis patients.Frontiers in medicine · 2025Article
- Development of a prognostic risk score to predict early mortality in incident elderly Japanese hemodialysis patients.PloS one · 2024Article
- 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
- Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis.Annals of medicine · 2023Article
- A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.Kidney360 · 2022Article
- Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study.Journal of medical Internet research · 2021Article
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6 authors.
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Abstract
backgroundThe first-year survival rate among patients undergoing hemodialysis remains poor. Current mortality risk scores for patients undergoing hemodialysis employ regression techniques and have limited applicability and robustness.
objectiveWe aimed to develop a machine learning model utilizing clinical factors to predict first-year mortality in patients undergoing hemodialysis that could assist physicians in classifying high-risk patients.
methodsTraining and testing cohorts consisted of 5351 patients from a single center and 5828 patients from 97 renal centers undergoing hemodialysis (incident only). The outcome was all-cause mortality during the first year of dialysis. Extreme gradient boosting was used for algorithm training and validation. Two models were established based on the data obtained at dialysis initiation (model 1) and data 0-3 months after dialysis initiation (model 2), and 10-fold cross-validation was applied to each model. The area under the curve (AUC), sensitivity (recall), specificity, precision, balanced accuracy, and F1 score were used to assess the predictive ability of the models.
resultsIn the training and testing cohorts, 585 (10.93%) and 764 (13.11%) patients, respectively, died during the first-year follow-up. Of 42 candidate features, the 15 most important features were selected. The performance of model 1 (AUC 0.83, 95% CI 0.78-0.84) was similar to that of model 2 (AUC 0.85, 95% CI 0.81-0.86).
conclusionsWe developed and validated 2 machine learning models to predict first-year mortality in patients undergoing hemodialysis. Both models could be used to stratify high-risk patients at the early stages of dialysis.
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