Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability

Abstract The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the risk factors of CRPA infection and construct a machine learning model to provide a prediction tool for clinical prevention and control. A total...

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Bibliographic Details
Main Authors: Yan Jiang, Hong-wei Wang, Fang-ying Tian, Yue Guo, Xiu-mei Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Reports
Subjects:
Carbapenem resistance
Pseudomonas aeruginosa
Machine learning models
XGBoost
Risk factors
Online Access:https://doi.org/10.1038/s41598-025-04028-x
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