Supervised Machine Learning to Identify Hospital Inpatients Needing a Change of Antibiotic Therapy in Real Time: Preclinical Diagnostic Evaluation and Feasibility Study
The prospective clinical evaluation demonstrated that the approach is feasible and achieves moderate predictive performance in real-world conditions.
Authors
- Laura Shallcross (ORCID: https://orcid.org/0000-0003-1713-2555)
- Michael Brown (ORCID: https://orcid.org/0000-0001-7205-8947)
- Jonathan Cooper (ORCID: https://orcid.org/0000-0001-6009-3542)
- R. Dobson (ORCID: https://orcid.org/0000-0002-2656-3579)
- Charles Curtis (ORCID: https://orcid.org/0000-0002-2992-1941)
- Haziq Chowdhury
- Neil Stone (ORCID: https://orcid.org/0000-0002-6887-7947)
- Surya K. De (ORCID: https://orcid.org/0000-0001-5014-0798)
- Steve Harris (ORCID: https://orcid.org/0000-0002-4982-1374)
- Peter Dutey‐Magni (ORCID: https://orcid.org/0000-0002-8942-9836)
- A Cawthorne
Institutions
- University of London (GB)
- University College London Hospitals NHS Foundation Trust (GB)
- King's College London (GB)
- Great Ormond Street Hospital for Children NHS Foundation Trust (GB)
- London Centre for Nanotechnology (GB)
- School of Advanced Study (GB)
- London School of Hygiene & Tropical Medicine (GB)
- MRC Clinical Trials Unit at UCL (GB)
- King's College Hospital NHS Foundation Trust (GB)
- University College London (GB)
Publication Details
- Journal
- Open Forum Infectious Diseases
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1093/ofid/ofaf721
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute for Health and Care Research
- Department of Health and Social Care
- University College London
- University College London Hospitals NHS Foundation Trust