Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis
- DOI
- 10.3389/fpubh.2026.1894150
- Published
- 2026-09-07
- Container
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fpubh.2026.1894150,
title = {Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis},
author = {David Chinaecherem Innocent and Rejoicing Chijindum Innocent and Increase Praise Innocent},
year = {2026},
journal = {Frontiers in Public Health},
doi = {10.3389/fpubh.2026.1894150},
url = {https://doi.org/10.3389/fpubh.2026.1894150}
}RIS
TY - JOUR TI - Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis AU - David Chinaecherem Innocent AU - Rejoicing Chijindum Innocent AU - Increase Praise Innocent PY - 2026 JO - Frontiers in Public Health DO - 10.3389/fpubh.2026.1894150 UR - https://doi.org/10.3389/fpubh.2026.1894150 ER -
APA
Innocent, D. C., Innocent, R. C., & Innocent, I. P. (2026). Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2026.1894150
Source records
- crossref · retrieved 2026-09-25T18:46:56.170Z