  Skin and soft tissue infections (SSTIs) are among the most frequently
observed diseases in ambulatory and hospital settings. Resistance of diverse
bacterial pathogens to antibiotics is a significant cause of severe SSTIs, and
treatment failure results in morbidity, mortality, and increased cost of
hospitalization. Therefore, antimicrobial surveillance is essential to predict
antibiotic resistance trends and monitor the results of medical interventions.
To address this, we developed machine learning (ML) models (deep and
conventional algorithms) to predict antimicrobial resistance using antibiotic
susceptibility testing (ABST) data collected from patients clinically diagnosed
with primary and secondary pyoderma over a period of one year. We trained an
individual ML algorithm on each antimicrobial family to determine whether a
Gram-Positive Cocci (GPC) or Gram-Negative Bacilli (GNB) bacteria will resist
the corresponding antibiotic. For this purpose, clinical and demographic
features from the patient and data from ABST were employed in training. We
achieved an Area Under the Curve (AUC) of 0.68-0.98 in GPC and 0.56-0.93 in GNB
bacteria, depending on the antimicrobial family. We also conducted a
correlation analysis to determine the linear relationship between each feature
and antimicrobial families in different bacteria. ML techniques suggest that a
predictable nonlinear relationship exists between patients'
clinical-demographic characteristics and antibiotic resistance; however, the
accuracy of this prediction depends on the type of the antimicrobial family.
