Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy

We constructed an XGBoost model and provided model interpretability using the SHAP. This model provides a basis for assessing the 28-day mortality risk of patients with SIC, aiding in clinical decision support and the formulation of personalized treatment strategies.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2026-02-04
DOI
https://doi.org/10.1186/s12911-026-03363-x
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy

Xianwei Zhang, Jianmin Wu, Baoxin Wang, Xiangyuan Ruan et al.
BMC Medical Informatics and Decision Making
Sepsis Diagnosis and Treatment
article

Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy

Xianwei Zhang, Jianmin Wu, Baoxin Wang, Xiangyuan Ruan, Yihua Dong, Xueyang Xu, Chenglong Liang
article en

Abstract

We constructed an XGBoost model and provided model interpretability using the SHAP. This model provides a basis for assessing the 28-day mortality risk of patients with SIC, aiding in clinical decision support and the formulation of personalized treatment strategies.

BMC Medical Informatics and Decision MakingVol. 26(1)
Chonnam National University (KR), Wenzhou University (CN), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), First Affiliated Hospital of Xiamen University (CN), Zhejiang Hospital (CN), Wenzhou City People's Hospital (CN), Shaoxing People's Hospital (CN), Zhejiang University (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 66%
Sepsis Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy — Xianwei Zhang, Jianmin Wu, et al. · BMC Medical Informatics and Decision Making (2026) | TGRS Research Map | TGRS