Development and application of an early prediction model for risk of bloodstream infection based on real-world study

This study successfully identified a set of 5 features by analyzing routine laboratory data clinical monitoring indicators among hospitalized patients. Based on this set, a machine learning-based early risk prediction model for BSI was constructed. The model is capable of early and rapid differentiation between BSI and non-BSI patients. The inclusion of minimal risk prediction factors enhances its applicability in clinical settings, particularly at the primary care level. To further improve the model's real-world applicability and more convenient for clinical use, the online application of the model could greatly improve the efficiency of BSI diagnosis and reducing patients' mortality.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2025-05-14
DOI
https://doi.org/10.1186/s12911-025-03020-9
Citations
4
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
5.42

Funders

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article

Development and application of an early prediction model for risk of bloodstream infection based on real-world study

Zhaohui Meng, Shenshen Zhi, Jiaxin Xie, Haorong Li et al.
4 citations
BMC Medical Informatics and Decision Making
Sepsis Diagnosis and Treatment
5.42
article

Development and application of an early prediction model for risk of bloodstream infection based on real-world study

Zhaohui Meng, Shenshen Zhi, Jiaxin Xie, Haorong Li, Yue Cheng, Wei Li, Li Yang, Xiefei Hu, Shu Tang, Haiping Fan
article en
4 citations

Abstract

This study successfully identified a set of 5 features by analyzing routine laboratory data clinical monitoring indicators among hospitalized patients. Based on this set, a machine learning-based early risk prediction model for BSI was constructed. The model is capable of early and rapid differentiation between BSI and non-BSI patients. The inclusion of minimal risk prediction factors enhances its applicability in clinical settings, particularly at the primary care level. To further improve the model's real-world applicability and more convenient for clinical use, the online application of the model could greatly improve the efficiency of BSI diagnosis and reducing patients' mortality.

BMC Medical Informatics and Decision MakingVol. 25(1)
Chongqing University of Posts and Telecommunications (CN), Chongqing University (CN), Beijing Institute of Big Data Research (CN), Beijing Enterprises (China) (CN), Chongqing Emergency Medical Center (CN)
Chongqing Municipal Education Commission, Wu Jieping Medical Foundation
Openalex Percentile: Top 4%
Sepsis Diagnosis and Treatment
5.42
DOIOpen Access PDFOpenAlex
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study — Zhaohui Meng, Shenshen Zhi, et al. · BMC Medical Informatics and Decision Making (2025) | TGRS Research Map | TGRS