
A machine learning model using random forest analysis improves the prediction of mortality risk in hospitalized patients with cirrhosis.
Key Details
- 1Study analyzed data from 121 hospitals worldwide participating in the CLEARED consortium.
- 2Random forest AI model outperformed traditional risk prediction methods for hospitalized cirrhosis patients.
- 3Model maintained accuracy across both high- and low-income countries.
- 4External validation was performed using National U.S. veterans’ data.
- 5Effective risk stratification was possible with just 15 variables.
- 6The tool grouped patients by mortality risk, supporting clinical decision-making.
Why It Matters
This study highlights the growing potential for AI to enhance clinical prognostication, supporting triage and care prioritization. AI models like this can be adapted to radiology and imaging datasets to similarly improve patient management and operational workflows.

Source
EurekAlert
Related News

•EurekAlert
AI Model Predicts Growth Spurts from Pediatric Neck X-rays for Orthodontics
Korean researchers developed an AI system (ARNet-v2) that predicts children's growth spurts from neck X-rays to enhance orthodontic treatment planning.

•EurekAlert
Imaging Reveals Skull Changes and Immune Impact in Glioblastoma
Advanced imaging uncovers that glioblastoma affects the skull and immune system, not just the brain.

•EurekAlert
AI-Based CT Analysis Predicts Outcomes in Fibrotic Lung Disease
AI analysis of one-year CT changes predicts disease progression and survival in fibrotic interstitial lung disease.