Artificial intelligence techniques used to obtain antibiotic resistance patterns

The Universidad Carlos III de Madrid (UC3M) is conducting research that analyses antibiotic resistance patterns with the aim of finding trends that can help decide which treatment to apply to each type of patient and stop the spread of bacteria. This study, recently published in the scientific journal Nature Communications, has been carried out together with the University of Exeter, the University of Birmingham (both in the United Kingdom) and the Westmead Hospital in Sydney (Australia). In order to observe a bacterial pathogen's resistance to an antibiotic in clinical environments, a measure called MIC (Minimum Inhibitory Concentration) is used, which is the minimum concentration of antibiotic capable of inhibiting bacterial growth. The greater the MIC of a bacterium against an antibiotic, the greater its resistance. However, most public databases only contain the frequency of resistant pathogens, which is aggregated data calculated from MIC measurements and predefined resistance thresholds. "For example, for a given pathogen, the antibiotic resistance threshold may be 4: if a bacterium has an MIC of 16, it is considered resistant and is counted when calculating the resistance frequency", says Pablo Catalán, lecturer and researcher in the UC3M Mathematics Department and author of the study. In this regard, the resistance reports that are carried out nationally and by organisations such as the WHO are prepared using this aggregated resistance frequency data.