ArticleThe Journal of infection2025
Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
Article in The Journal of infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Early Prediction of Carbapenem Resistance in Gram-Negative Organisms at Culture Ordering from Routinely Collected Hospital Data: Temporal and External Validation of ORDER-CARB.Infectious diseases and therapy · 2026Article
- Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.BJUI compass · 2026Article
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals.European journal of hospital pharmacy : science and practice · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Co-Resistance Structure and Multidrug Resistance-Associated Antimicrobials inAntibiotics (Basel, Switzerland) · 2026Article
- An AI-Driven Antimicrobial Resistance Surveillance Framework for Low-Resource Settings: A Pilot Study from Mogadishu, Somalia.Infection and drug resistance · 2026Article
- Evolution of technology and disparities in antimicrobial stewardship: a narrative review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
- Development of a predictive model using automated machine learning for Carbapenem-resistant Organisms (CRO) infections in hospitalized patients.Frontiers in microbiology · 2026Article
- Development and internal-external validation of a machine learning-based risk prediction model for multidrug resistance in mechanically ventilated ICU patients with chronic obstructive pulmonary disease.Frontiers in medicine · 2026Article
- A consensus-weighted multi-agent ensemble feature selection framework for antibiotic susceptibility prediction in pediatric respiratory diseases.Frontiers in artificial intelligence · 2026Article
- Machine learning based prediction of antimicrobial resistanceFrontiers in public health · 2026Article
- The Impact of COVID-19 on the Epidemiology of Carbapenem Resistance.Antibiotics (Basel, Switzerland) · 2025Review
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6 authors.
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Abstract
backgroundPatients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
methodsWe used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021. Model performance was evaluated by comparing predictions to final microbiology results in test datasets from 01-January-2022 to 31-December-2023 and to clinicians' prescribing.
findings4709 infection episodes were used for model training and evaluation; antibiotic resistance rates ranged from 7-67%. In held-out test data, resistance prediction performance was similar for the seven antibiotics (AUCs 0.680 [95%CI 0.641-0.720] to 0.737 [0.674-0.797]). Performance improved for most antibiotics when species identifications (available ∼24 h later) were included as model inputs (AUCs 0.723 [0.652-0.791] to 0.827 [0.797-0.857]). In patients treated with a beta-lactam, clinician prescribing led to 70% receiving an active beta-lactam: 44% were over-treated (broader spectrum treatment than needed), 26% optimally-treated (narrowest spectrum active agent), and 30% under-treated (inactive beta-lactam). Model predictions without species data could have led to 79% of patients receiving an active beta-lactam: 45% over-treated, 34% optimally-treated, and 21% under-treated.
conclusionsPredicting AMR in bloodstream infections is challenging for both clinicians and models. Despite modest performance, machine learning models could still increase the proportion of patients receiving active empirical treatment by up to 9% over current clinical practice in an environment prioritising antimicrobial stewardship.
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