Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection.
Journal:
The Journal of hospital infection
PMID:
37004787
Abstract
BACKGROUND: Machine learning (ML) models for early identification of patients at risk of hospital-acquired urinary tract infection (HA-UTI) may enable timely and targeted preventive and therapeutic strategies. However, clinicians are often challenged in the interpretation of the predictive outcomes provided by the ML models, which often reach different performances.