Using Machine Learning for predicting antibiotic resistance in bloodstream infections

Antibiotic resistance poses a significant challenge in modern medicine, with current tests requiring 24-48 hours to determine susceptibility. In critical cases, this delay can be life-threatening. Therefore, a rapid and accurate method for predicting antibiotic resistance is essential. This purpose of this study is the development and comparison of several machine learning/deep learning models to predict antibiotic resistance or susceptibility using epidemiological data. While most studies focus on urinary tract infections or all infections, due to the availability of large datasets, few address specifically bloodstream infections despite their clinical importance. Models were trained using hospital data acquired from the Bologna metropolitan area, for patients with positive blood cultures between January 2024 and December 2024. The following pathogen-antibiotic combinations were considered for this study: S.aureum-Oxacillin, E.faecium-Vancomycin, E.coli-Cefotaxime, Ceftazidime and K.pneumoniae-Cefotaxime, Meropenem. Input features include patients demographics (age, sex), time the blood culture was taken, identified species, antibiotic used and AMR rates in the different hospitals. Models were built using Python and the sklearn and PyTorch libraries. The employed models were logistic regression, random forest, XGBoost and Multi Layer Perceptron. The predictive value of the machine learning models is modest, with values for the ROC-AUC ranging from 0.60 to 0.82, depending on the pathogen-antibiotic combination used. These values are similar to previous results found in literature. Important predictive features such as historical antibiotic exposure and patient medical records were unavailable in the current dataset. Future iterations will incorporate these and other clinically relevant variables to optimize model performance and enhance predictive precision.