Deep learning for the identification of multidrug resistance in MALDI-TOF MS samples of Escherichia coli

Research studying the prediction of antibiotic resistance based on mass spectrometry data and machine learning focuses only on simple models for the identification of resistance to one antibiotic at a time, Even though a problem of multidrug resistance is currently being faced. Therefore, in this study, a multi-label approach for classifying multidrug resistance in Escherichia coli samples using raw MALDI-TOF mass spectrometry data and deep learning techniques was developed. The spectra from a recently published public database, encompassing over 4,500 samples of the bacteria under study, were utilized, sufficient for training a deep learning model, specifically a one dimensional convolutional neural network for this case. The use of this architecture proves to be highly efficient, achieving weighted AUROC and AUPRC values equal to or greater than 0.80, as well as a general performance calculated using the Hamming loss metric reaching 0.132. These results demonstrate that the use of deep learning allows for the development of complex models that enable the simultaneous identification of a predefined set of antibiotics, aiding in the determination of a highly effective treatment.