Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.

Journal: The Journal of infection
PMID: 39742978

Abstract

BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.

Authors

  • Kevin Yuan
    Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Augustine Luk
    Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Jia Wei
    Department of Thyroid Surgery, The First Hospital of Jilin University Changchun 130021 P. R. China mengxiany@mail.jlu.edu.cn.
  • A Sarah Walker
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • Tingting Zhu
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
  • David W Eyre
    John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford and Public Health England, Oxford, UK.

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