Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.

Journal: BMC medical informatics and decision making
PMID: 31443706

Abstract

BACKGROUND: A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible.

Authors

  • Ross J Burton
    Department of Infection Sciences, Severn Pathology, Bristol, BS10 5NB, UK. BurtonRJ@cardiff.ac.uk.
  • Mahableshwar Albur
    Department of Infection Sciences, Severn Pathology, Bristol, BS10 5NB, UK.
  • Matthias Eberl
    Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK. Electronic address: eberlm@cf.ac.uk.
  • Simone M Cuff
    Division of Infection and Immunity, School of Medicine, Cardiff University, Henry Wellcome Building, Heath Park, Cardiff, CF14 4XN, UK.

Keywords

External Resources

Popular Topics
Recent Journals