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
MD Anderson Unveils New AI Genomics Insights and Therapeutic Advances
MD Anderson reports breakthroughs in cancer therapeutics and provides critical insights into AI models for genomic analysis.
•EurekAlert
SH17 Dataset Boosts AI Detection of PPE for Worker Safety
University of Windsor researchers released SH17, a 8,099-image open dataset for AI-driven detection of personal protective equipment (PPE) in manufacturing settings.
•EurekAlert
AI Powers Breakthroughs in Optical Metasurface Design for Imaging
A review highlights how AI is revolutionizing the design of optical metasurfaces, advancing compact optics and computational imaging.