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The resulting model (random forest expanded sensitive = CarbaDetector) predicts carbapenemase production with a sensitivity of 96.6% (CI: 93.5–98.5%) and a specificity of 85.0% (CI: 78.2–90.4%), resulting in only 8.7% of isolates that need to be further tested being false positives, which is a significant decrease when compared to the EUCAST and CA-SFM algorithm (Supplementary Data 1). Assessing the performance of the novel CA-SFM algorithm and the EUCAST screening process To set the baseline for our model, we assessed the CA-SFM algorithm and the EUCAST screening algorithm for carbapenemase detection by applying it to all three datasets, using WGS results as ground truth. Validation of our algorithm using external datasets To further validate the trained model and its correct prediction of CPE, the resulting model (CarbaDetector) has been used firstly to predict carbapenemase production on a set of 282 Enterobacterales isolates from Switzerland (University of Zurich) with and without carbapenemase production (external dataset A, included in Supplementary Data 2). Secondly, prediction of carbapenemase production on incomplete datasets (where not all eight recommended antibiotic disks were used) was tested on a different, previously published dataset containing the disk diffusion diameters of 518 Enterobacterales isolates submitted for carbapenemase testing to the French reference laboratory for multidrug-resistant Gram-negatives (external dataset B, included in Supplementary Data 3, originally used for the assessment of the CA-SFM algorithm16).