machine learning

Objective. To systematically synthesize factors associated with the presence, colonization, infection, or acquisition of bacterial pathogens with clinically significant antimicrobial resistance phenotypes; to assess the variability of available quantitative estimates of these associations; and, separately, to characterize prediction models, clinical scores, and algorithms developed to estimate the probability of such outcomes. Materials and Methods. A systematic review was conducted of publications identified through a PubMed/MEDLINE search covering the period from January 1, 2000, to April 30, 2026, using terms related to risk factors and prediction, antimicrobial resistance, and clinical infectious syndromes.

Antimicrobial resistance poses a significant global threat to healthcare systems worldwide. The high cost and short lifespan of new antibiotics due to the rapid evolution of resistance exacerbate this crisis, prompting exploration of diverse …

Objective. Development and validation of a suite of mathematical models to predict national-level antimicrobial resistance (AMR) trends by integrating antimicrobial consumption (AMC) and current AMR data. Materials and Methods. Data on systemic antimicrobial consumption across 82 regions of the Russian Federation (2008–2022; source: IQVIA) and AMR levels (2013–2022; source: AMRmap.ru) were analyzed. The workflow included regional name normalization, exclusion of antimicrobials with low clinical relevance, calculation of moving averages (3–10 years) for AMR parameters, and AMC quantification in Defined Daily Doses (DDD).