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

TL;DR

This paper aims to create machine learning models for antibiotic resistance that are both accurate and easy for clinicians to understand.

Contribution

A novel approach that treats resistance genes as independent features and integrates additional markers for transparency.

Findings

Resistance genes are treated as independent features to improve model clarity.

The method integrates curated SNPs and contextual markers for better clinical alignment.

The approach supports scalable and transparent antibiotic resistance predictions.

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.