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Development and validation of interpretable machine learning models to predict intensive care unit outcomes in patients on hemodialysis: a multicenter study

Abstract Background Hemodialysis patients are at high risk for ICU admission due to elevated mortality, cardiovascular disease, and infection rates. Traditional ICU scoring systems (e.g., APACHE-II, SOFA) demonstrate limited accuracy in this population. This study aimed to identify key risk factors...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Minjie Chen, Pengan Li, Yuanwen Xu, Zhenghui Li, Yan Xiong, Jianhua Wu, Chintan Pandya, Yunuo Wang, Guixin Huang
Format: Artigo
Sprache:Inglês
Veröffentlicht: BMC 2025-12-01
Schriftenreihe:BMC Medical Informatics and Decision Making
Schlagworte:
Predictive model
Machine learning
Interpretable
Hemodialysis
Intensive care unit
Multicenter study
Online-Zugang:https://doi.org/10.1186/s12911-025-03301-3
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