AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A stage prediction model for sepsis-associated acute kidney injury (SA-AKI) based on interpretable machine learning is developed, thereby providing support for individualized clinical intervention.
A CatBoost-based machine learning model incorporating 32 clinically accessible variables showed good predictive performance for in-hospital mortality in patients with diabetes and SA-AKI, supporting early risk stratification and clinical decision-making.
AIMS/BACKGROUND Sepsis is a leading cause of morbidity and mortality in intensive care units (ICUs). Early identification of patients at high risk of death is essential to guide timely interventions and optimize resource allocation. This study aimed to develop and validate an interpretable machine learning model for pr...
We developed and externally validated an interpretable machine-learning model for observed continuous renal replacement therapy (CRRT) initiation in sepsis-associated acute kidney injury (SA-AKI) using a strict 24-hour landmark framework. This multicenter retrospective study used MIMIC-IV and eICU-CRD data. Predictors...
Abstract Background Sepsis-induced coagulopathy (SIC) is a common and severe complication in patients with sepsis, characterized by microvascular thrombosis, systemic endothelial damage, and markedly increased short-term mortality. Existing traditional clinical risk scoring systems demonstrate limited accuracy and fail...
An interpretable Random Forest model employing eight routinely available indicators accurately predicts AKI in septic MM patients, bridging algorithmic complexity and bedside applicability, and may facilitate early risk stratification and personalized renal protection in this high-risk group.
BACKGROUND Early mortality risk stratification remains challenging in intensive care unit (ICU) patients with acute upper gastrointestinal bleeding (AUGIB). We developed a parsimonious, interpretable ensemble model and evaluated its performance across independent ICU cohorts. METHODS MIMIC-IV was used for model devel...
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