Balancing complexity and clarity-towards clinician-ready antibiotic resistance prediction models.

MOTIVATION: The escalating challenge of antibiotic resistance (ABR) demands clinician-ready machine learning models that are not only accurate but interpretable. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: Not applicable.