A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database

The XGBoost model had the best predictive performance, providinga robust tool to guide personalized therapy and optimize critical care resource allocation.

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Publication Details

Journal
Scandinavian Journal of Gastroenterology
Published
2026-02-06
DOI
https://doi.org/10.1080/00365521.2026.2625822
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database

Chaoze Cheng, Dingmin Wang, Sujuan Fei, Xu Cao et al.
Scandinavian Journal of Gastroenterology
Sepsis Diagnosis and Treatment
article

A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database

Chaoze Cheng, Dingmin Wang, Sujuan Fei, Xu Cao, Wenling Li
article en

Abstract

The XGBoost model had the best predictive performance, providinga robust tool to guide personalized therapy and optimize critical care resource allocation.

Scandinavian Journal of Gastroenterology
Xuzhou Medical College (CN), Affiliated Hospital of Xuzhou Medical College (CN)
Openalex Percentile: Top 64%
Sepsis Diagnosis and Treatment
DOISourceOpenAlex
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