# 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.

## Key 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.