Use of artificial intelligence techniques for the study of bacterial resistance to antimicrobials
Year: 2024
Type: article
Abstract: Excessive and incorrect use of antibiotics can lead to artificial selection of germs, which often develop resistance to drugs. Artificial Intelligence (AI) is an area of computing that has demonstrated a significant capacity to support predictive solutions in health care issues. Indeed, this article aims to present possibilities for the use of AI for the approach of bacterial resistance to drugs. The literature – with a defined search strategy – is then surveyed using DeCS (https://decs.bvsalud.org/) and PubMed (https://pubmed.ncbi.nlm.nih.gov/). Thirty articles were selected – whose information was added to those obtained in complementary texts selected by the authors – for the elaboration of the text. The results and discussion were organized in the following sections: (1) k Nearest Neighbor (k-NN), (2) Bayesian Networks, (3) Decision Trees, (4) Artificial Neural Networks and (5) Support Vector Machines. The use of AI techniques can be very useful for the study of antimicrobial resistance mechanisms, which qualifies these tools to support the control of the emergence of resistant and multiresistant pathogens.
Source: Scire Salutis
Authors: Fabiola Alves Alcantara, Pedro Henrique Penedo Mendonça, Sara Muniz Miranda, Marcella Martins Souto, Andreia Patrícia Gomes, Eugenio Silva, Rodrigo Siqueira Batista
Language: English
Subfield: Molecular Medicine
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4411734856