
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

Source
EurekAlert
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