ABSTRACT
This study explored the efficacy of electronic phenotyping in data labeling for machine learning with a focus on urinary tract infections (UTIs). We contrasted labels from electronic phenotyping against previously published labels such as urine culture positivity. In comparison, electronic phenotyping showed the potential to enhance specificity in UTI labeling while maintaining similar sensitivity and was easily scaled for application to a large dataset suitable for machine learning, which we used to train and validate a machine learning model. Electronic phenotyping offers a valuable method for machine learning label generation in healthcare, with potential benefits for patient care and antimicrobial stewardship. Further research will expand its application and optimize techniques for increased performance.