Affiliation:
1. Department of Healthcare Genetics and Genomics, Clemson University, Clemson, SC 29634, USA
Abstract
Machine learning is being increasingly applied in various aspects of medicine. The availability of large amounts of digital health records has enabled researchers to apply machine learning algorithms to tackle different medical problems. Urinary tract infections (UTIs) are common bacterial infections that are prone to being misdiagnosed and over-treated with antibiotics. For appropriate tailored antibiotic therapy, new diagnostic methods providing rapid pathogen identification and antibiotic susceptibility testing are urgently needed. In this review, we first discuss emerging technologies that have employed machine learning models to deliver speedy diagnostic results, particularly for urinary tract infections. We then explore how machine learning models are enabling sequence-based diagnostics by predicting antibiotic resistances from genome sequencing data. Finally, we examine different studies that apply machine learning to electronic health records to improve UTI diagnosis, to reduce antibiotic use and guide treatments without urine culture, and to reduce clinical workload and unnecessary hospital visits.