Bethesda, MD (July 18, 2025) — 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,” said Dr. Jasmohan S. Bajaj, the study's corresponding author.
Key findings:
Explore the model in action here: https://silveys.shinyapps.io/app_cleared/.
This paper is one of three studies recently published on this topic in the American Gastroenterological Association’s journals. One was a worldwide consensus statement on organ failures , including liver in cirrhosis patients, while the second study identified specific blood markers and complications that influence the risk of in-hospital death , focusing on liver failure biomarkers.
“Liver disease is one of the most underappreciated causes of death worldwide — alcohol, viral hepatitis, and late diagnoses are major drivers,” Bajaj said. “When someone is hospitalized, it’s often because everything upstream — prevention, screening, primary care — has already failed.”
Contact for media: Annie Mehl, media@gastro.org , 301-272-0013
About the AGA Institute
The American Gastroenterological Association is the trusted voice of the GI community. Founded in 1897, AGA represents members from around the globe who are involved in all aspects of the science, practice, and advancement of gastroenterology. The AGA Institute administers the practice, research, and educational programs of the organization. www.gastro.org
AGA is on Instagram .
Like AGA on Facebook.
Follow us on X @AmerGastroAssn and Bluesky @amergastroassn.bsky.social .
Check out our videos on YouTube .
Join AGA on LinkedIn .
Gastroenterology
Enhancement of Inpatient Mortality Prognostication with Machine Learning in a Prospective Global Cohort of Patients with Cirrhosis with External Validation