Development and testing of a machine-learning-based system for early prediction of antimicrobial susceptibility probability univamu machinelearning AI antimicrobial microbe microbiology
By Tarun Sai LomteFeb 7 2023Reviewed by Danielle Ellis, B.Sc. In a recent study posted to Research Square*, researchers developed and tested a machine learning -based clinical decision support system to predict antibiotic resistance.
Major species isolated were Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, and Enterococcus faecalis. The antibiogram data were stratified into the 2014-19 and 2020 datasets. Culture types and species exhibited heterogeneity in susceptibility to single antibiotics. Susceptibility rates were also different across sample types; it was 56% to amoxicillin-clavulanate in urine samples but 35% in samples from the lower respiratory tract.
Related StoriesML algorithms included AdaBoost , gradient boosting , random forest , bagging , extreme gradient boosting , neural networks , and logistic regression . Four stages were defined: 1) sampling, 2) direct examination, 3) culture and 4) species identification. Models were trained to predict antibiotic susceptibility probability to 22 single and 25 combination antibiotics for isolates of the 2020 validation dataset.
Next, the team examined model performance for rare situations by plotting the least frequent situations only. The aggregate AUC decreased from 0.73 to 0.65 when limited to the rarest situations. LR performed the poorest at all stages, while BAY models performed fair, reaching high average AUC values for culture and species stages.
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