Journal of critical careHumansMachine LearningAcute Kidney InjuryMaleDisease Progression
Machine learning survival analysis for predicting kidney disease progression in patients with acute kidney injury undergoing continuous kidney replacement therapy: An analysis of the LINKA database.
Donghwan Yun, Ari Hong, Kwangsoo Kim, Jeonghwan Lee, Yaerim Kim, Kyubok Jin, Ji Eun Kim, Shin Young Ahn, Gang-Jee Ko, Seokwoo Park, Sejoong Kim, Hee-Yeon Jung, Jang-Hee Cho, Sun-Hee Park, Eun Sil Koh
Published: 202610.1016/j.jcrc.2025.155419
Abstract
PURPOSE: The progression of acute kidney injury (AKI) to end-stage kidney disease (ESKD) poses challenges due to high risks of comorbidities and poor outcomes. This study aimed to develop and validate machine learning survival models for predicting E…
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