Predicting future hospital antimicrobial resistance prevalence using machine learning
Year-to-year resistance has generally changed little within Trust-pathogen-antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions.
Authors
- A. Sarah Walker (ORCID: https://orcid.org/0000-0002-0412-8509)
- Rebecca Guy (ORCID: https://orcid.org/0000-0001-5500-5691)
- Berit Müller‐Pebody (ORCID: https://orcid.org/0000-0002-8874-480X)
- Karina-Doris Vihta (ORCID: https://orcid.org/0000-0002-4111-7577)
- Susan Hopkins (ORCID: https://orcid.org/0000-0001-5179-5702)
- Russell Hope (ORCID: https://orcid.org/0000-0003-2838-6833)
- David A. Clifton (ORCID: https://orcid.org/0000-0002-9848-8555)
- Koen B. Pouwels (ORCID: https://orcid.org/0000-0001-7097-8950)
- Emma Pritchard (ORCID: https://orcid.org/0000-0002-0963-9260)
- David W. Eyre (ORCID: https://orcid.org/0000-0001-5095-6367)
- Dimple Chudasama (ORCID: https://orcid.org/0000-0002-2214-5900)
- Katherine L. Henderson (ORCID: https://orcid.org/0000-0003-2918-3341)
Institutions
- John Radcliffe Hospital (GB)
- Open Data Institute (GB)
- National Institute for Health and Care Research (GB)
- University of Oxford (GB)
- Suzhou Research Institute (CN)
- Science Oxford (GB)
- UK Health Security Agency (GB)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1038/s43856-024-00606-8
- Citations
- 11
- Primary Topic
- Antibiotic Use and Resistance
- Type
- article
- Field-Weighted Citation Impact
- 3.28
Funders
- National Institute for Health Research Health Protection Research Unit
- National Institute for Health and Care Research
- Royal Academy of Engineering
- University of Oxford
- Oxford University Hospitals NHS Foundation Trust
- NIHR Oxford Biomedical Research Centre