Predicting antibiotic susceptibility in urinary tract infection with artificial intelligence-model performance in a multi-centre cohort.

Alfred Lok Hang LeeCurtis Chun Kit ToRonald Cheong Kin ChanJanus Siu Him WongGrace Chung Yan LuiIngrid Yu Ying CheungViola Chi Ying ChowChristopher Koon Chi LaiMargaret IpRaymond Wai Man Lai

Published in: JAC-antimicrobial resistance (2024)

Random forest model may aid judicious empirical antibiotics use in UTI. Given the reasonable performance and accuracy, these accurate models may aid clinicians in choosing between different first-line antibiotics for UTI.

Keyphrases

urinary tract infection
artificial intelligence
machine learning
big data
deep learning
climate change
high resolution
palliative care
mass spectrometry
neural network