Clinical explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection
Author:
Publisher
Elsevier BV
Subject
Infectious Diseases,Microbiology (medical),General Medicine
Reference46 articles.
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3. ECDC, EMEA. The bacterial challenge: time to react. 2009.
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3. A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models;Health Informatics Journal;2024-01
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