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.

No abstract

TensorFlow is an open source artificial intelligence system from Google Inc., which was announced in October 2015. With flexibility, high efficiency, and good scalability and portability, it can be applied to a variety of computing environments from smartphones to large computing clusters. It is currently used in many fields. This study experimentally proposes a technical solution, based on TensorFlow to construct a convolutional neural network model for face recognition, and counts the number of faces identified, so as to quickly perform attendance statistics.