Year: 2025
Type: article
Abstract: OBJECTIVES: The 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. METHODS: This 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. RESULTS: Eight 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%. CONCLUSIONS: This 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.
Source: Healthcare Informatics Research
Authors: Hyeji Kim, Hyekyung Woo
Institutions: Kongju National University, Soongeui Women's College
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $300
Award: G5140343637
OpenAlex ID: W4416086000