An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks.

ABSTRACT: Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as dru

SUBMITTER: De Waele G 

PROVIDER: S-EPMC11563574 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature