Research graph
References from Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis. Local targets link to admitted publications; unresolved targets remain external evidence.
Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective
10.1128/cmr.00179-21 · 2022 · External reference
Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: a systematic review considering antimicrobial susceptibility tests in real-world healthcare settings
10.1371/journal.pone.0319460 · 2025 · External reference
AI—prediction of Neisseria gonorrhoeae resistance at the point of care from genomic and epidemiologic data
10.3390/healthcare13141643 · 2025 · External reference
The accuracy of molecular detection targeting the mutation C2611T for detecting moderate-level azithromycin resistance in Neisseria gonorrhoeae: a systematic review and meta-analysis
10.3390/antibiotics10091027 · 2021 · External reference
Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests
10.1111/nyas.14549 · 2023 · External reference
Global trends of antimicrobial resistance rates in Neisseria gonorrhoeae: a systematic review and meta-analysis
10.3389/fphar.2024.1284665 · 2024 · External reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
10.1136/bmj.n71 · 2021 · External reference
Unresolved reference
2024 · External reference
PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews
10.1186/s12913-014-0579-0 · 2014 · External reference
Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews
10.1016/j.jclinepi.2005.02.022 · 2005 · External reference
Personalizing the empiric treatment of gonorrhea using machine learning models
10.1371/journal.pdig.0000549 · 2024 · External reference
Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes
10.1128/spectrum.01703-23 · 2023 · External reference
Using genomic data and machine learning to predict antibiotic resistance: a tutorial paper
10.1371/journal.pcbi.1012579 · 2024 · External reference
Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models
10.1016/j.sjbs.2022.02.047 · 2022 · External reference
A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria
10.3389/frabi.2024.1405296 · 2024 · External reference
Unlocking antimicrobial resistance with multiomics and machine learning
10.1016/j.tim.2025.04.017 · 2025 · External reference
Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance
10.1128/mbio.00488-25 · 2025 · External reference
Leveraging artificial intelligence to combat antimicrobial resistance in geriatric care
10.1177/20552076251346612 · 2025 · External reference
Neisseria gonorrhoeae antimicrobial resistance: the future of antibiotic therapy
10.3390/jcm12247767 · 2023 · External reference
A systematic review of Neisseria gonorrhoeae drug resistance development in South Africa
10.1007/s42770-024-01281-6 · 2024 · External reference
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
10.1016/j.jclinepi.2019.02.004 · 2019 · External reference
