Whole-genome sequencing of clinical Escherichia coli urine isolates used to evaluate AI-based antibiotic susceptibility prediction
This study contains whole-genome sequencing data from clinical Escherichia coli isolates recovered from urine samples. The isolates were used in a prospective clinical evaluation of an artificial intelligence-based method for predicting antibiotic susceptibility from partial antibiotic susceptibility testing results and patient metadata. The sequencing data were generated to characterize resistance determinants and to investigate associations between genotypic resistance patterns and prediction errors.
This study contains whole-genome sequencing data from clinical Escherichia coli isolates recovered from urine samples. The isolates were used in a prospective clinical evaluation of an artificial intelligence-based method for predicting antibiotic susceptibility from partial antibiotic susceptibility testing results and patient metadata. The sequencing data were generated to characterize resistance determinants and to investigate associations between genotypic resistance patterns and prediction errors.
UNIVERSITY OF GOTHENBURG