{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T18:52:53Z","timestamp":1794336773351,"version":"4.9.0"},"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec017\">\n<jats:title>Background<\/jats:title>\n<jats:p>Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the efficacy of machine learning (ML) approaches in predicting AMR in critical and high-priority pathogens (CHPP), considering antimicrobial susceptibility tests in real-world healthcare settings.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec018\">\n<jats:title>Methods<\/jats:title>\n<jats:p>The search methodology encompassed the examination of several databases, such as PubMed\/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO. An extensive electronic database search was conducted from the inception of these databases until November 2024.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec019\">\n<jats:title>Results<\/jats:title>\n<jats:p>After completing the final step of the eligibility assessment, the systematic review ultimately included 21 papers. All included studies were cohort observational studies assessing 688,107 patients and 1,710,867 antimicrobial susceptibility tests. GBDT, Random Forest, and XGBoost were the top-performing ML models for predicting antibiotic resistance in CHPP infections. GBDT exhibited the highest AuROC values compared to Logistic Regression (LR), with a mean value of 0.80 (range 0.77\u20130.90) and 0.68 (range 0.50\u20130.83), respectively. Similarly, Random Forest generally showed better AuROC values compared to LR (mean value 0.75, range 0.58\u20130.98 versus mean value 0.71, range 0.61\u20130.83). However, some predictors selected by these algorithms align with those suggested by LR.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec020\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>ML displays potential as a technology for predicting AMR, incorporating antimicrobial susceptibility tests in CHPP in real-world healthcare settings. However, limitations such as retrospective methodology for model development, nonstandard data processing, and lack of validation in randomized controlled trials must be considered before applying these models in clinical practice.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0319460","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T18:46:45Z","timestamp":1740509205000},"page":"e0319460","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":49,"title":["Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-1416","authenticated-orcid":true,"given":"Carlos 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