ReviewAntibiotics (Basel, Switzerland)2023
Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.
Review in Antibiotics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it, 146 citations in OpenAlex.
- Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.Molecular biotechnology · 2025Pooled it
- Availability and Key Characteristics of National Early Warning Systems for Emerging Profiles of Antimicrobial Resistance in High-Income Countries: Systematic Review.JMIR public health and surveillance · 2025Pooled it
- Antimicrobial resistance analysis of Klebsiella pneumoniae bloodstream infections based on a random forest algorithm: a longitudinal study based on data from tertiary hospitals in China from 2012 to 2023.International health · 2026Article
- ALPAR: automated learning pipeline for antimicrobial resistance.Bioinformatics (Oxford, England) · 2026Article
- A custom GPT-based model for the automated analysis and interpretation of antimicrobial susceptibility tests in Gram-negative bacteria.JAC-antimicrobial resistance · 2026Article
- Prediction of Antimicrobial Resistance in People Living With Cystic Fibrosis Using Machine Learning.MedComm · 2026Article
- From Species Identification to Empirical Therapy: A Machine Learning and Rule-Based Decision Support Framework for Antifungal Resistance Prediction in ICUMedical sciences (Basel, Switzerland) · 2026Article
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Patients With Sepsis: Case Study.JMIR medical informatics · 2026Article
- Antimicrobial use and Escherichia coli resistance patterns in Hungarian pig farms: a data-driven farm-level analysis.Scientific reports · 2026Article
- Postoperative complications of coronary artery bypass grafting: a narrative review on pathophysiology, management strategies, and the emerging role of artificial intelligence.Cardiovascular diagnosis and therapy · 2026Review
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Article
- Machine Learning-Based Analysis of Large-Scale Transcriptomic Data Identifies Core Genes Associated with Multi-Drug Resistance.International journal of molecular sciences · 2026Article
- Recent Advances and Future Perspectives in the Detection of Carbapenem-ResistantInfection and drug resistance · 2026Review
- Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance inFrontiers in microbiology · 2026Article
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Predictive modelling of the dynamics of antimicrobial resistance: creation of a bank of renewable models based on machine learning.Frontiers in pharmacology · 2026Article
- Interpreting antimicrobial resistance from bacterial whole-genome sequencing: prediction tools, database fragmentation, analytical trade-offs, and harmonized reporting.Frontiers in microbiology · 2026Review
- An explainable artificial intelligence framework reveals mutations associated with drug resistance inBiosafety and health · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.