Researchers employed a machine learning technique known as random forest analysis and found that it significantly outperformed traditional methods in predicting which hospitalized patients with cirrhosis are at risk of death, according to a new paper published in Gastroenterology.
This gives us a crystal ball – it helps hospital teams, transplant centers, GI and ICU services to triage and prioritize patients more effectively.”
Dr. Jasmohan S. Bajaj, study’s corresponding author
Key findings:
- Data analyzed from 121 hospitals worldwide, which were part of the CLEARED consortium.
- The model performed consistently across both high- and low-income countries.
- It was validated using National U.S. veterans’ data and…
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