Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit

The RF model is reliable in early identifying high-risk IS patients for VAP. The SHAP method offers clear and intuitive explanations for individual risk assessment. The web-based tool has the potential to improve clinical outcomes by promptly recognizing patients at increased VAP risk and facilitating early intervention, further multicenter prospective studies are required to validate its generalizability and practical utility.

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Publication Details

Journal
Frontiers in Neurology
Published
2025-05-07
DOI
https://doi.org/10.3389/fneur.2025.1513732
Citations
2
Primary Topic
Nosocomial Infections in ICU
Type
article
Field-Weighted Citation Impact
3.09

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article

Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit

Songran Yang, Heshan Cao, Jian‐Jun Wei, Ping Hua
2 citations
Frontiers in Neurology
Nosocomial Infections in ICU
3.09
article

Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit

Songran Yang, Heshan Cao, Jian‐Jun Wei, Ping Hua
article en
2 citations

Abstract

The RF model is reliable in early identifying high-risk IS patients for VAP. The SHAP method offers clear and intuitive explanations for individual risk assessment. The web-based tool has the potential to improve clinical outcomes by promptly recognizing patients at increased VAP risk and facilitating early intervention, further multicenter prospective studies are required to validate its generalizability and practical utility.

Frontiers in NeurologyVol. 16
Sun Yat-sen University (CN), Sun Yat-sen Memorial Hospital (CN), First Affiliated Hospital of Guangzhou University of Chinese Medicine (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 10%
Nosocomial Infections in ICU
3.09
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