A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators

The Random Forest model developed in this study can provide some guidance for clinical decision-making in SALI patients, but further validation is still required and should only be implemented in clinical practice after further research.

Authors

Publication Details

Journal
Medical Principles and Practice
Published
2026-02-14
DOI
https://doi.org/10.1159/000550910
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators

Huimin Chong, Shaoli Deng, Yiming Yang, Jing Lv et al.
Medical Principles and Practice
Sepsis Diagnosis and Treatment
article

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators

Huimin Chong, Shaoli Deng, Yiming Yang, Jing Lv, Jinmi Li, Wenjun Zhu
article en

Abstract

The Random Forest model developed in this study can provide some guidance for clinical decision-making in SALI patients, but further validation is still required and should only be implemented in clinical practice after further research.

Medical Principles and Practice
Good health and well-being
Openalex Percentile: Top 54%
Sepsis Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators — Huimin Chong, Shaoli Deng, et al. · Medical Principles and Practice (2026) | TGRS Research Map | TGRS