ArticleHealthcare informatics research2025
Deep Learning-Based Death Prediction Model for Chronic Kidney Disease.
Article in Healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Toward Explainable Precision Nephrology: Machine Learning-Based Chronic Kidney Disease Prediction.Biomedicines · 2026Article
- Predicting Chronic Kidney Disease from Biomarkers: An Explainable Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Recent advancements and future prospects on AI-integrated sensing techniques for non-invasive chronic kidney disease diagnosis: a review.Frontiers in artificial intelligence · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectivesThe prevalence of chronic kidney disease (CKD) continues to rise, making it one of the leading causes of death worldwide. Recent advances in medical and health research have progressed beyond traditional statistical methodologies, increasingly leveraging artificial intelligence to identify and predict factors influencing mortality. Further AI-based research is therefore essential to deepen understanding of the determinants of death among CKD patients.
methodsThis study used data from the Korea Disease Control and Prevention Agency's in-depth survey of patients discharged between 2016 and 2021. Least absolute shrinkage and selection operator (LASSO) regression, a machine learning technique, was applied to identify significant factors associated with death in CKD patients. These selected variables were then incorporated into a deep learning-based predictive model.
resultsEight factors influencing death were identified, including length of hospital stay (coefficient = 0.023), emergency admission (0.016), age (0.013), severity-adjusted score (0.008), and regional differences (0.003). The developed deep learning model achieved a loss value of 0.1207 and an accuracy of 96.84%.
conclusionsThis study identified emergency visits and prolonged hospital stays as key predictors of death in CKD patients. To mitigate these risks, regular monitoring by nephrology specialists and timely initiation of renal replacement therapy are essential. Age also emerged as a critical determinant, emphasizing the importance of age-stratified clinical guidelines amid global aging trends. The high-performing, simplified predictive model based on general characteristics offers a valuable tool for rapid prognosis assessment in primary and secondary healthcare settings.
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Registered trials
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