[Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].

Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.

Authors

Huaizheng Liu (ORCID: https://orcid.org/0009-0003-0131-9515)
Chuanzheng Sun (ORCID: https://orcid.org/0000-0001-8284-8264)
Jing Qi (ORCID: https://orcid.org/0000-0001-9558-6742)
Hongwei Duan (ORCID: https://orcid.org/0000-0003-2841-3344)

Institutions

Central South University (CN)
Third Xiangya Hospital (CN)

Publication Details

Journal: PubMed
Published: 2025-06-28
DOI: https://doi.org/10.11817/j.issn.1672-7347.2025.250191
Primary Topic: Artificial Intelligence in Healthcare
Type: article
Field-Weighted Citation Impact: 0.00

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article

[Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].

Huaizheng Liu, Chuanzheng Sun, Jing Qi, Hongwei Duan

Jun 28, 2025

PubMed

Artificial Intelligence in Healthcare

article

[Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].

Huaizheng Liu, Chuanzheng Sun, Jing Qi, Hongwei Duan

June 28, 2025article en

Abstract

Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.

PubMedVol. 50(6)

Central South University (CN), Third Xiangya Hospital (CN)

Openalex Percentile: Top 70%

Artificial Intelligence in Healthcare

DOI Open Access PDF OpenAlex

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