ArticleScientific reports2025
Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Response to comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper".PLoS computational biology · 2026Article
- Comparison of OneChoice AI-Based Clinical Decision Support Recommendations with Infectious Disease Specialists and Non-Specialists for Empirical Urinary Tract Infection Therapy in Lima, Peru.Diagnostics (Basel, Switzerland) · 2026Article
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Article
- Two Worlds, One Battle: How Bacteria and Malignancies Converge on Drug Resistance.International journal of molecular sciences · 2026Review
- Improving the Precision of Etiological Diagnosis in Bacterial Infections Using Molecular Technologies: A Comparative Analysis of Platforms, AI Integration, and Point-of-Care Deployment.International journal of molecular sciences · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Substandard and Falsified Antibiotics Seized in Belgium: Quality Control Analysis Reveals High Prevalence of WHO Watch List Molecules and Bioavailability Non-Compliance.Antibiotics (Basel, Switzerland) · 2026Article
- Machine learning based prediction of antimicrobial resistanceFrontiers in public health · 2026Article
- Surveillance-Driven Machine Learning for Prediction of Antimicrobial Susceptibility: An Explainable Modeling Framework using the Pfizer ATLAS Dataset (2004 - 2023).Wellcome open research · 2026Article
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- A consensus-weighted multi-agent ensemble feature selection framework for antibiotic susceptibility prediction in pediatric respiratory diseases.Frontiers in artificial intelligence · 2026Article
- The Gut Microbiome and Colistin Resistance: A Hidden Driver of Antimicrobial Failure.International journal of molecular sciences · 2025Review
- Molecular resistance mechanisms to newly approved antibiotics (2017-2025) in WHO priority pathogens.Frontiers in microbiology · 2025Review
- Role of artificial intelligence in bacterial diagnostics and surveillance of anti-microbial resistance.Digital healthReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 consistently outperformed other models, achieving AUC values of 0.96 and 0.95 for the Phenotype-Only and Phenotype + Genotype sets, respectively. Hyperparameter tuning yielded slight accuracy improvements, while data balancing techniques notably increased recall. Across all models, the antibiotic used emerged as the most influential feature in predicting resistance outcomes. The SHAP summary plots generated provide insights into model interpretability. Our findings provide valuable insights into global AMR patterns and demonstrate the potential of AI-driven approaches for resistance prediction to help inform clinical decision-making and support the formulation of effective AMR mitigation policies, subject to the availability of highly granular datasets.
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