Rethinking ACLF Prognosis: Can Machine Learning Outperform Conventional Scores? Editorial on: Predictive Machine Learning Model in ICU Patients with Acute-on-Chronic Liver Failure and Two or More Organ Failures

Jung Hee Kim

doi:10.3350/cmh.2025.1053

Articles

Page Path

Editorial

Rethinking ACLF Prognosis: Can Machine Learning Outperform Conventional Scores? Editorial on: Predictive Machine Learning Model in ICU Patients with Acute-on-Chronic Liver Failure and Two or More Organ Failures

Jung Hee Kim

DOI: https://doi.org/10.3350/cmh.2025.1053 [Accepted]

Published online: September 23, 2025

Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea

Corresponding author: Jung Hee Kim,
Email: mazyyang5@gmail.com

Received: 15 September 2025 • Accepted: 17 September 2025

903 Views
53 Download
0 Crossref
0 Scopus

Full Article

Download PDF

Keywords: Acute-on-Chronic Liver Failure; Cirrhosis; Machine Learning; Prognostication

Citations

Citations to this article as recorded by

Cite

CITE

export

Copy Download

Format

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Rethinking ACLF Prognosis: Can Machine Learning Outperform Conventional Scores? Editorial on: Predictive Machine Learning Model in ICU Patients with Acute-on-Chronic Liver Failure and Two or More Organ Failures

DOI: https://doi.org/10.3350/cmh.2025.1053

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format: