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Machine Learning for Urinary Tract Infection Prediction in Emergency Departments: An Explainable Approach

2025-12-18 health informatics Title + abstract only
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Urinary tract infections (UTIs) represent a substantial burden in emergency department (ED) settings, where diagnostic delays and the limitations of traditional clinical assessments often result in suboptimal treatment decisions. This study develops an interpretable machine learning framework to enhance real-time UTI prediction accuracy. We analyzed a retrospective dataset of 80,387 ED patient encounters from four institutions (2013-2016), encompassing 220 clinical variables. Four machine learn...

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