Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.

ABSTRACT: Antimicrobial resistance is a growing global health threat, and artificial intelligence offers a promising avenue for developing advanced tools to address this challenge. In this study, we applied various machine learning techniques to predict bacterial antibiotic resistance using the Pfizer ATLAS Antibiotics dataset. This comprehensive dataset includes patient demographic data, sample collection details, antibiotic susceptibility test results, and resistance phenotypes for 917,049 bacterial isolates. The dataset was divided into two subsets: Phenotype-Only and Phenotype + Genotype, excluding and including 589,998 isolates with genotype data, respectively. Both subsets underwent exploratory data analysis, preprocessing, machine learning model training, validation, and optimization. XGBoost

SUBMITTER: Valavarasu S 

PROVIDER: S-EPMC12368220 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature