Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jul 27, 2023
Open Peer Review Period: Jul 27, 2023 - Aug 11, 2023
Date Accepted: Jan 8, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Prediction of Antibiotic Resistance in Patients with Urinary Tract Infection: Algorithm Development and Validation
ABSTRACT
Background:
The early prediction of antibiotic resistance in patients with urinary tract infection is important to guide appropriate antibiotic therapy selection.
Objective:
In the present study, we aimed to predict antibiotic resistance in patients with urinary tract infection. Additionally, we aimed to interpret the machine learning models we developed.
Methods:
We used admission, diagnosis, prescription, and microbiology records of patients who underwent urine culture tests in Yongin Severance Hospital, South Korea. We developed 5 sub-models to classify urinary tract infection pathogens as either sensitive or resistant to cephalosporin, piperacillin/tazobactam, trimethoprim/sulfamethoxazole, fluoroquinolone, and carbapenem. To analyze how each variable contributed to the machine learning model’s predictions of antibiotic resistance, we used the SHapley Additive exPlanations method. Finally, we proposed a prototype machine learning based clinical decision support system to provide clinicians the resistance probabilities for each antibiotic.
Results:
The area under the curve values ranged from 0.710 to 0.826 in the training set and 0.642 to 0.812 in the test set for predicting antibiotic resistance. The administration of drugs before infection and exposure time to these drugs were found to be important variables for predicting antibiotic resistance.
Conclusions:
The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with urinary tract infection. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with urinary tract infection.
Citation
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Copyright
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