Early Prediction of Hepatic Decompensation in Cirrhosis Using Optimised <scp>XGBoost</scp> Models at the Initial Outpatient Hepatology Visit

Optimised XGBoost models demonstrated robust predictive accuracy for medium- and long-term hepatic decompensation among patients with compensated cirrhosis. These models may support early risk stratification and enable personalised management strategies to prevent clinical deterioration.

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Publication Details

Journal
Liver International
Published
2026-02-23
DOI
https://doi.org/10.1111/liv.70560
Primary Topic
Liver Disease and Transplantation
Type
article
Field-Weighted Citation Impact
0.00
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article

Early Prediction of Hepatic Decompensation in Cirrhosis Using Optimised XGBoost Models at the Initial Outpatient Hepatology Visit

Caelan Stephanson, Micah Grubert Van Iderstine, Gregory Jackson, Sem Perez et al.
Liver International
Liver Disease and Transplantation
article

Early Prediction of Hepatic Decompensation in Cirrhosis Using Optimised XGBoost Models at the Initial Outpatient Hepatology Visit

Caelan Stephanson, Micah Grubert Van Iderstine, Gregory Jackson, Sem Perez, Tyler Szun, GY Minuk, Megan Weisshaar, Nabiha Faisal
article en

Abstract

Optimised XGBoost models demonstrated robust predictive accuracy for medium- and long-term hepatic decompensation among patients with compensated cirrhosis. These models may support early risk stratification and enable personalised management strategies to prevent clinical deterioration.

Liver InternationalVol. 46(4)
University of Manitoba (CA)
Openalex Percentile: Top 46%
Liver Disease and Transplantation
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