Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease

Linhui Hu; Hao Cheng; Jing Pan; Suthat Liangpunsakul; Yan Wang

doi:10.1080/07853890.2026.2621522

January 30, 2026Annals of MedicineOpen Access

Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease

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Key Points

The research aims to identify key risk factors for acute-on-chronic liver failure in patients with alcohol-associated liver disease using machine learning.
Developed a clinical prediction model combining logistic regression (LR) and machine learning techniques.
Analyzed key clinical markers including TBIL, folate, VitB12, and ΔPT.
Focused on risk stratification for timely interventions in ALD patients.
Identified TBIL, folate, VitB12, and ΔPT as significant prognostic markers.
The model potentially reduces the incidence of acute-on-chronic liver failure in ALD patients.
Early risk stratification enables timely clinical interventions.

Implication

A prediction model identifies risk factors for acute-on-chronic liver failure in alcohol-related disease, suggesting improved patient outcomes.

Abstract

We developed a robust clinical prediction model combining LR and machine learning approaches. TBIL, folate, VitB12 and ΔPT are key prognostic markers that may enable early risk stratification and timely intervention, potentially reducing ACLF incidence in ALD patients.

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Cite This Study

Hu et al. (2026) studied this question.

synapsesocial.com/papers/6980fe57c1c9540dea810500 https://doi.org/10.1080/07853890.2026.2621522

View Full Paper

Ask AI

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Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease

Key Points

Implication

Abstract

Discussion

Cite This Study

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