Comparative Analysis of Machine Learning Models for Predicting Hospital- and Community-Associated Urinary Tract Infections Using Demographic, Hospital, and Socioeconomic Predictors.
Journal:
The Journal of hospital infection
Published Date:
May 6, 2025
Abstract
BACKGROUND: Urinary tract infections (UTI) are among the most common infections encountered in both community and healthcare settings. Differentiating between community-associated UTI (CA-UTI) and healthcare-associated UTI (HA-UTI) is crucial for understanding their epidemiology, identifying risk factors, and developing appropriate treatment strategies. Machine learning (ML) techniques have shown significant potential in improving the accuracy of predicting these infections, enabling more effective interventions and better patient outcomes. While previous studies have demonstrated the utility of ML models in various healthcare settings, there is still a need for a comparative analysis of different ML approaches, particularly in distinguishing between CA-UTI and HA-UTI and assessing the risk of UTI among hospitalized patients.
Authors
Keywords
No keywords available for this article.