Construction and validation of a mortality prediction model for patients with sepsis-associated encephalopathy: Interpretable machine learning approach

Validated across two large critical care databases, the interpretable XGBoost model serves as a reliable tool for mortality prediction in SAE patients, which may help clinicians identify high-risk SAE patients early and optimize management to improve patient outcomes.

Authors

Lingling Ge (ORCID: https://orcid.org/0000-0003-0972-2264)
Yongkui Zhu
Xiaoli Chen (ORCID: https://orcid.org/0000-0003-0536-1553)
Xia Cheng (ORCID: https://orcid.org/0000-0001-5421-4715)
Ziyi Wang (ORCID: https://orcid.org/0009-0002-8164-7457)

Institutions

Soochow University (CN)
Zhangjiagang First People's Hospital (CN)

Publication Details

Journal: Digital Health
Published: 2025-05-01
DOI: https://doi.org/10.1177/20552076251393281
Primary Topic: Sepsis Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

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article

Construction and validation of a mortality prediction model for patients with sepsis-associated encephalopathy: Interpretable machine learning approach

Lingling Ge, Yongkui Zhu, Xiaoli Chen, Xia Cheng et al.

May 1, 2025

Digital Health

Sepsis Diagnosis and Treatment

article

Construction and validation of a mortality prediction model for patients with sepsis-associated encephalopathy: Interpretable machine learning approach

Lingling Ge, Yongkui Zhu, Xiaoli Chen, Xia Cheng, Ziyi Wang

May 1, 2025article en

Abstract

Validated across two large critical care databases, the interpretable XGBoost model serves as a reliable tool for mortality prediction in SAE patients, which may help clinicians identify high-risk SAE patients early and optimize management to improve patient outcomes.

Digital HealthVol. 11

Soochow University (CN), Zhangjiagang First People's Hospital (CN)

Openalex Percentile: Top 57%

Sepsis Diagnosis and Treatment

DOI Open Access PDF OpenAlex

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