Balancing complexity and clarity-towards clinician-ready antibiotic resistance prediction models.
Where this comes from
- Record sourced from PubMed, PMID 41032281.
- Also identified by DOI 10.1093/bioinformatics/btaf556 and PMC identifier 12518920.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The escalating challenge of antibiotic resistance (ABR) demands clinician-ready machine learning models that are not only accurate but interpretable. By treating resistance genes as independent features and augmenting them with curated single-nucleotide polymorphisms and contextual markers, this approach delivers scalable, transparent predictions aligned with clinical decision-making needs. Not applicable.
Medical subject headings
- Machine Learning
- Drug Resistance, Microbial
- Drug Resistance, Bacterial